Systems and methods for quantifying and/or verifying ocean-based interventions for sequestering carbon dioxide

ABSTRACT

A method for calculating carbon credits includes obtaining sensor data associated with at least a portion of a deployment for cultivating a target product in a body of water, executing at least one model based at least in part on the sensor data to generate an output predicting at least one characteristic associated with the target product, the deployment, or a portion of the body of water, and inputting the output into a quantification model. The quantification model is executed to generate an output associated with a predicted capacity of the target product to sequester carbon dioxide. An accuracy of the predicted capacity resulting from the output of the quantification model is greater than an accuracy of a predicted or inferred capacity resulting from the output of each model individually. Carbon dioxide offset credits are determined based on the predicted capacity resulting from the output of the quantification model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 63/251,321, filed Oct. 1, 2021, entitled,“Systems and Methods for Quantifying and/or Verifying Target ProductAccumulation for Greenhouse Gas Sequestration,” the disclosure of whichis incorporated herein by reference in its entirety.

BACKGROUND

The present disclosure relates generally to carbon sequestration, andmore particularly, to systems and methods for quantifying and/orverifying ocean-based interventions for sequestering carbon dioxide.

Human activity has increased atmospheric carbon dioxide (CO2) byapproximately 50% (from about 280 to about 420 ppm) over the past200-300 years due to the combustion of fossil fuels, land use changes,and other industrial processes. These anthropogenic increases inatmospheric CO2 are causing a variety of environmental and societalproblems, including global warming, increased wildfires, increaseddroughts, increased severity and frequency of storms, sea level rise,melting glaciers, and ocean acidification.

The global carbon cycle operates through a variety of response andfeedback mechanisms between the Earth's primary carbon reservoirs,namely the marine and terrestrial biospheres, the atmosphere, the ocean,and sediments/rocks. With respect to atmospheric carbon dioxide, thecarbon cycle can be broken down into two distinct, but overlapping,components: the fast carbon cycle and the slow carbon cycle. The fastcarbon cycle encompasses the movement of carbon via photosynthesis andrespiration, as well as the continuous exchange of CO2 amongst thebiosphere, atmosphere, and ocean. The fast carbon cycle is dynamic andvolatile, and it can be best understood as the flow of carbon throughliving ecosystems. In contrast, the slow carbon consists of the movementof carbon via gravity, pressure, chemical weathering, ocean currents,etc. These processes move carbon from living ecosystems into geologicaland deep ocean reservoirs such as sediments, mineral deposits (e.g.,oil, gas, coal), and deep waters. Slow carbon cycle reservoirs evolvevery slowly.

One of the greatest challenges facing humanity in the 21st century is todevelop scalable methods for removing CO2 from the atmosphere and upperocean (e.g., CO2 in the fast carbon cycle) and durably sequestering itin, for example, deep ocean, marine sediments, geological deposits,and/or the like (e.g., in or by the slow carbon cycle) in order to limitthe environmental and socio-economic damage that is associated withincreasing CO2 in the atmosphere and upper ocean. Without humaninterference, carbon moves from the slow to the fast cycle over millionsof years through volcanic activity, driven by the subduction and meltingof limestones and oil and gas-bearing rocks, and over intermediatetimescales through ocean upwelling, and carbon cycling between theatmosphere, ocean, biosphere, and geologic reservoirs, in both the fastand slow carbon cycles, is generally balanced in a manner that promotesstable climates, ocean chemistry, and ecosystems. These geologictimelines, however, are much too slow to address the challenges we facetoday due to anthropogenic increases in atmospheric CO2.

In an attempt to abate CO2 emissions (and/or other greenhouse gasemissions), governments and regulatory authorities have establishedgreenhouse gas emissions caps and have allowed organizations to complywith the emissions caps by purchasing, for example, carbon creditsand/or offsets. Carbon credits can be bought and sold as amounts ofcarbon sequestered using carbon sequestration technology. Companies thatachieve preset carbon offsets (e.g., becoming “carbon neutral”) areoften rewarded with financial incentives and/or tax benefits, which canbe used to subsidize future projects for the reduction of greenhouse gasemissions.

Ocean-based interventions such as cultivating marine mass and sinking itto the ocean floor have shown promise as carbon sequestrationtechnologies. Predicting the growth of marine species and/or itscapacity to sequester carbon dioxide can, for example, enable thecapacity to be bought and/or sold as carbon credits in a suitable marketsuch as commodities market, futures market etc. Therefore, it can beadvantageous to predict the growth of marine species and/or its capacityto sequester carbon dioxide. Existing methodologies to assess and/orpredict the growth of marine species rely on human observation and areoften imprecise, inaccurate, labor intensive, and/or impracticable forlarge scale deployments. Existing methodologies for assessing and/orpredicting a result of other ocean-based interventions for sequesteringcarbon dioxide face similar challenges and/or uncertainties.

Accordingly, a need exists for improved systems and methods forquantifying and/or verifying ocean-based interventions for sequesteringcarbon dioxide.

SUMMARY

Systems and methods for quantifying and/or verifying ocean-basedinterventions for sequestering carbon dioxide. In some embodiments, amethod can include obtaining sensor data associated with at least aportion of a deployment for cultivating a target product in a body ofwater, executing at least one model based on the sensor data to generatean output predicting at least one characteristic associated with thetarget product, the deployment, and/or a portion of the body of water,and inputting the output into a quantification model. The quantificationmodel is executed to generate an output associated with a predictedcapacity of the target product to sequester carbon dioxide and a carbondioxide offset credit is determined based on the predicted capacityresulting from output of the quantification model. An accuracy of thepredicted capacity resulting from the output of the quantification modelcan be greater than an accuracy of a predicted capacity resulting fromthe output of each model individually.

In some embodiments, a method can include obtaining sensor dataassociated with a deployment for cultivating a target product in a bodyof water. The method can also include providing at least a portion ofthe sensor data as an input to at least one model from a number ofmodels associated with the target product, the deployment, and/or aportion of the body of water in which the deployment is disposed. Themodels are executed in a predetermined sequence such that an output of acurrent model is an input for at least one subsequently executed modelin the predetermined sequence. An output of a last model executed in thesequence is provided as input to a quantification model, which isexecuted to generate an output associated with a predicted capacity ofthe target product to sequester carbon dioxide.

In some embodiments, a method can include obtaining first sensor datafrom at least one sensor associated with at least one cultivationapparatus for cultivating a target product and second sensor data fromat least one sensor associated with a deployment of any number ofcultivation apparatus. The deployment being deployed in an ocean. The atleast one cultivation apparatus being included in the plurality ofcultivation apparatus. A first model is trained, based at least in parton the first sensor data, to generate a first output predicting at leastone parameter associated with a growth of the target product of the atleast one cultivation apparatus, and a second model is trained, based atleast in part on the second sensor data, to generate a second outputpredicting a geographic dispersion of the deployment in the ocean. Themethod further includes training a third model, based at least in parton the first output and the second output, to generate a third outputpredicting an amount of accumulation of the target product of thedeployment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic illustration of a system for quantifying targetproduct accumulation, according to an embodiment.

FIG. 1B is a schematic illustration of a server device included in thesystem of FIG. 1A.

FIG. 2 is a flow chart of an example method of determining carbondioxide offset credits, according to an embodiment.

FIG. 3 is a flowchart illustrating a method and/or process of training areinforced calibration model, according to some embodiments.

FIG. 4 is a flowchart illustrating a method and/or process of executingan aggregated field observations model, according to some embodiments.

FIG. 5 is a flowchart illustrating a method and/or process of training areinforced dispersion model, according to some embodiments.

FIG. 6 is a flowchart illustrating a method and/or process of executingan aggregated population dynamics model, according to some embodiments.

FIG. 7 is a flowchart illustrating a method and/or process of executingan aggregated environmental metric model, according to some embodiments.

FIG. 8 is a flowchart illustrating a method and/or process of training areinforced growth model, according to some embodiments.

FIG. 9 is a flowchart illustrating a method and/or process of executingan aggregated growth response model, according to some embodiments.

FIG. 10 is a flowchart illustrating a method and/or process of executinga carbon credit quantification model, according to some embodiments.

FIG. 11 is a flowchart illustrating a method and/or process of executinga carbon credit quantification model, according to some embodiments.

FIG. 12 is a flowchart of an example method for determining accumulationof target product in a deployment that can be used to determine carbondioxide offset credits, according to some embodiments.

DETAILED DESCRIPTION

Systems and methods for quantifying and/or verifying target productaccumulation for greenhouse gas sequestration (e.g., carbonsequestration) is described herein. The target product, as describedherein, includes and/or encompasses a wide variety of species. Forexample, a “target product” can include but is not limited to aquaticand/or marine species such as calcifying organisms, plankton, archaeafilter feeders (e.g., oysters, clams, etc.), bacteria and othermicroorganisms, heterokonts like algae(s) (e.g., microalgae, macroalgae,etc.), and/or the like. In other implementations, however, a targetproduct can refer to any suitable species whose cultivation leads to adesired result (e.g., as a harvested product, for bioremediation, forcarbon capture and sequestration, and/or the like). In someimplementations, the target products described herein can be cultivatedand/or used for the purpose of bioremediation, eventualcultivation/harvesting, and/or for sequestering carbon dioxide. Thetarget products may generally include negatively, neutrally, and/orpositively buoyant species (e.g., species that sink, remain suspended,or float in water as they grow). Such target products may propagate orreproduce by producing gametophytes and/or sporophytes that can rapidlygrow in a body of water and sequester atmospheric carbon viaphotosynthesis.

The target products described herein can be select marine species who'snatural and/or desired habitat is a body of water. When referring to abody of water, it should be understood that the body of water can beselected based on characteristics that may facilitate the cultivation ofthe target product. Accordingly, though specific bodies of water may bereferred to herein (e.g., an ocean or sea), it should be understood thatthe embodiment, example, and/or implementation so described is notlimited to use in such an environment unless the context clearly statesotherwise. Moreover, the term “saltwater” as used in this specificationis intended to refer to any body of water the constituents of whichinclude a certain concentration of salt(s). In contrast “freshwater” canrefer to any body of water the constituents of which do not include orinclude limited concentrations of salt(s). Saltwater, for example, canrefer to the water forming oceans, seas, bays, gulfs, as well as surfaceand/or subsurface brines, etc. Freshwater, for example, can refer to thewater forming rivers, lakes, etc. Moreover, bodies of water describedherein can also include certain mixtures of freshwater and saltwater(generally known as “brackish”) such as, for example, the mixture ofriver water and sea water found in estuaries and/or the like.

Many marine target products (e.g., macroalgae) show promise as a carbonsequestration pathway as their wild growth currently contributes tonaturally occurring carbon sequestration to the seafloor. Target productcultivation has the potential to improve this sequestration ratesignificantly due to increased cultivation productivity andsinking/sequestration rate relative to these naturally occurringphenomena. Target products can be cultivated in oceans, estuaries,lakes, rivers, and/or any other suitable body of water. These targetproducts can be allowed to grow and accumulate biomass. Biomass may becorporeally retained or eroded (allowed to naturally break off and sink)into the water. Typically, after the accumulation reaches a certainthreshold value, the target products are allowed to sink (or caused tosink) to the seafloor, thereby effectively sequestering the carbondioxide associated with the accumulated target product.

Accordingly, carbon credits can be associated with the accumulation ofthe target product and/or capacity of the target product to sequestercarbon. For instance, an amount of carbon that can be sequestered perunit of target product (e.g., that is sunk to the bottom of a body ofwater) can be calculated and/or predicted and sold in a carbon creditmarket (or any other suitable market) as a credit. In some instances,predicting growth, performance characteristics, and/or the capacity ofthe target product to sequester carbon can for example, enable thepredicted capacity to be bought and/or sold as a commodity (e.g., in acommodities market, in a futures market, and/or in any other suitablemarket). Accordingly, accurately predicting target product accumulationand/or erosion can be useful to calculate carbon dioxide offset credits.Currently, however, there are no known existing systems and/or methodsfor predicting target product accumulation and/or erosion to the levelof accuracy suitable for determining and/or predicting carbon creditsassociated with the sequestration of the target product. Directmeasurement of target product accumulation and/or erosion through humanobservation can be infeasible and/or impracticable for large scaledeployments. Therefore, the systems and methods disclosed herein can beused to characterize, quantify, and/or predict carbon dioxide offsetcredits with desired degree of accuracy.

In some instances, systems and/or methods can use and/or implement acombination of multiple models (e.g., machine learning models,probabilistic models, statistical models, stochastic models, acombination thereof, and/or the like) to determine carbon dioxide offsetcredits. For example, a quantification model can receive as input,sensor data that is associated with a deployment and/or a portion of adeployment (e.g., one or more cultivation apparatus as discussed below)for cultivating target product. The quantification model can alsoreceive outputs from at least one model from the multiple models. Eachof these models can predict, for example, one or more characteristicsassociated with the target product, one or more characteristicsassociated with the deployment and/or the portion of the deployment, oneor more characteristics associated with an environment in which thedeployment and/or the portion of the deployment is deployed, and/or anyother suitable characteristic. Executing the quantification model cangenerate an output that can predict and/or that can be used to predict acapacity of the target product of the deployment to sequester carbondioxide. In some instances, carbon dioxide offset credits can becalculated based on the predicted capacity of the target product tosequester carbon dioxide. Since the quantification model uses theoutputs from multiple models, the quantification model can predict thecapacity of the target product with a higher degree of accuracy than aprediction based on each individual model.

In some embodiments, a method can include obtaining sensor dataassociated with at least a portion of a deployment for cultivating atarget product in a body of water, executing at least one model based onthe sensor data to generate an output predicting at least onecharacteristic associated with the target product, the deployment,and/or a portion of the body of water, and inputting the output into aquantification model. The quantification model is executed to generatean output associated with a predicted capacity of the target product tosequester carbon dioxide and a carbon dioxide offset credit isdetermined based on the predicted capacity resulting from output of thequantification model. An accuracy of the predicted capacity resultingfrom the output of the quantification model can be greater than anaccuracy of a predicted capacity resulting from the output of each modelindividually.

In some embodiments, a method can include obtaining sensor dataassociated with a deployment for cultivating a target product in a bodyof water. The method can also include providing at least a portion ofthe sensor data as an input to at least one model from a number ofmodels associated with the target product, the deployment, and/or aportion of the body of water in which the deployment is disposed. Themodels are executed in a predetermined sequence such that an output of acurrent model is an input for at least one subsequently executed modelin the predetermined sequence. An output of a last model executed in thesequence is provided as input to a quantification model, which isexecuted to generate an output associated with a predicted capacity ofthe target product to sequester carbon dioxide.

In some embodiments, a method can include obtaining first sensor datafrom at least one sensor associated with at least one cultivationapparatus for cultivating a target product and second sensor data fromat least one sensor associated with a deployment of any number ofcultivation apparatus. The deployment being deployed in an ocean. The atleast one cultivation apparatus being included in the plurality ofcultivation apparatus. A first model is trained, based at least in parton the first sensor data, to generate a first output predicting at leastone parameter associated with a growth of the target product of the atleast one cultivation apparatus, and a second model is trained, based atleast in part on the second sensor data, to generate a second outputpredicting a geographic dispersion of the deployment in the ocean. Themethod further includes training a third model, based at least in parton the first output and the second output, to generate a third outputpredicting an amount of accumulation of the target product of thedeployment.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to limit the full scope of theclaims. Unless defined otherwise, all technical and scientific termsused herein have the same meanings as commonly understood by one ofordinary skill in the art.

As used in this specification, the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.For example, the term “a member” is intended to mean a single member ora combination of members, “a material” is intended to mean one or morematerials, or a combination thereof. With respect to the use ofsubstantially any plural and/or singular terms herein, those havingskill in the art can translate from the plural to the singular and/orfrom the singular to the plural as is appropriate to the context and/orapplication. The various singular/plural permutations may be expresslyset forth herein for sake of clarity.

In general, terms used herein, and especially in the appended claims,are generally intended as “open” terms (e.g., the term “including”should be interpreted as “including but not limited to,” the term“having” should be interpreted as “having at least,” etc.). For example,the terms “comprise(s)” and/or “comprising,” when used in thisspecification, are intended to mean “including, but not limited to.”While such open terms indicate the presence of stated features, integers(or fractions thereof), steps, operations, elements, and/or components,they do not preclude the presence or addition of one or more otherfeatures, integers (or fractions thereof), steps, operations, elements,components, and/or groups thereof, unless expressly stated otherwise.

As used herein the term “and/or” includes any and all combinations ofone or more of the associated listed items. Said another way, the phrase“and/or” should be understood to mean “either or both” of the elementsso conjoined (i.e., elements that are conjunctively present in somecases and disjunctively present in other cases). It should be understoodthat any suitable disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,contemplate the possibilities of including one of the terms, either ofthe terms, or both terms. Other elements may optionally be present otherthan the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B” can referto “A” only (optionally including elements other than “B”), to “B” only(optionally including elements other than “A”), to both “A” and “B”(optionally including other elements), etc.

As used herein, “or” should be understood to have the same meaning as“and/or” as defined above. For example, when separating items in a list,“or” or “and/or” shall be interpreted as being inclusive (e.g., theinclusion of at least one, but also including more than one, of a numberor list of elements, and, optionally, additional unlisted items). Onlyterms clearly indicated to the contrary, such as when modified by “onlyone of” or “exactly one of” (e.g., only one of “A” or “B,” “A” or “B”but not both, and/or the like) will refer to the inclusion of exactlyone element of a number or list of elements.

As used herein, the phrase “at least one,” in reference to a list of oneor more elements, should be understood to mean at least one elementselected from any one or more of the elements in the list of elements,but not necessarily including at least one of each and every elementspecifically listed within the list of elements and not excluding anycombinations of elements in the list of elements, unless expresslystated otherwise. This definition also allows that elements mayoptionally be present other than the elements specifically identifiedwithin the list of elements to which the phrase “at least one” refers,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, “at least one of A and B” (or,equivalently, “at least one of A or B” or “at least one of A and/or B”)can refer to one or more “A” without “B,” one or more “B” without “A,”one or more “A” and one or more “B,” etc.

All ranges disclosed herein are intended to encompass any and allpossible subranges and combinations of subranges thereof unlessexpressly stated otherwise. Any listed range should be recognized assufficiently describing and enabling the same range being broken downinto at least equal subparts unless expressly stated otherwise. As willbe understood by one skilled in the art, a range includes eachindividual member and/or a fraction of an individual member whereappropriate.

As used herein, the terms “about,” “approximately,” and/or“substantially” when used in connection with stated value(s) and/orgeometric structure(s) or relationship(s) is intended to convey that thevalue or characteristic so defined is nominally the value stated orcharacteristic described. In some instances, the terms “about,”“approximately,” and/or “substantially” can generally mean and/or cangenerally contemplate a value or characteristic stated within adesirable tolerance (e.g., plus or minus 10% of the value orcharacteristic stated). For example, a value of about 0.01 can include0.009 and 0.011, a value of about 0.5 can include 0.45 and 0.55, a valueof about 10 can include 9 to 11, and a value of about 100 can include 90to 110. Similarly, a first surface may be described as beingsubstantially parallel to a second surface when the surfaces arenominally parallel. While a value, structure, and/or relationship statedmay be desirable, it should be understood that some variance may occuras a result of, for example, manufacturing tolerances or other practicalconsiderations (such as, for example, the pressure or force appliedthrough a portion of a device, conduit, lumen, etc.). Accordingly, theterms “about,” “approximately,” and/or “substantially” can be usedherein to account for such tolerances and/or considerations.

Referring to the drawings, FIG. 1 is a schematic illustration of asystem 100 for quantifying target product accumulation and/or erosion,according to an embodiment. The target product can be cultivated on orin a deployment 102 deployed in a suitable body of water (e.g., estuary,ocean, etc.). The deployment 102 can include any number of cultivationapparatus 104. Each cultivation apparatus 104 can include one or moresensors 106 to sense, detect, measure, capture, and/or quantify one ormore characteristics and/or images relevant to the species of targetproduct disposed on the cultivation apparatus 104. The sensors 106 oneach cultivation apparatus 104 can be communicably coupled to a server114 via a network 108. In some instances, the server 114 can also becommunicably coupled to one or more external data sources 110 via thenetwork 108.

As discussed above, target product(s) can include and/or encompass awide variety of species including but not limited to microalgae,macroalgae, plankton, marine bacteria, archaea filter feeders (such asoysters or clams), and/or crustaceans. The target product can be grownon a deployment 102 deployed in a suitable water body. The deployment102 can be made up of any number cultivation apparatus 104 (described infurther detail below). For instance, deployment 102 can include and/orcan be an assembly of several cultivation apparatus 104. In someembodiments, a deployment 102 can include and/or can be an assembly often(s) of cultivation apparatus 104. In some embodiments, a deployment102 can include and/or can be an assembly of hundred(s) of cultivationapparatus 104. In some embodiments, a deployment 102 can include and/orcan be an assembly of thousand(s) of cultivation apparatus 104. In someembodiments, a deployment 102 can include and/or can be an assembly often(s) of thousands of cultivation apparatus 104. In some embodiments, adeployment 102 can include and/or can be an assembly of hundred(s) ofthousands of cultivation apparatus 104. In some embodiments, adeployment 102 can include and/or can be an assembly of million(s) ofcultivation apparatus 104. In some embodiments, a deployment 102 caninclude and/or can be an assembly of more than a billion cultivationapparatus 104.

The cultivation apparatus 104 can be any suitable shape, size, and/orconfiguration. In some embodiments, for example, the cultivationapparatus 104 can be similar to or substantially the same as any of thecultivation apparatus described in U.S. Pat. No. 11,382,315 (the “'315patent”), filed Jun. 8, 2021, entitled “Systems and Methods for theCultivation of Target Product;” U.S. Provisional Patent Application No.63/278,243 (the “'243 provisional”), filed Nov. 11, 2021, entitled“Systems and Methods for Monitoring Accumulation of a Target Product;”U.S. Provisional Patent Application No. 63/323,285 (the “'285provisional”), filed Mar. 24, 2022, entitled “Floating Substrates forOffshore Cultivation of Target Products and Methods of Making and Usingthe Same;” U.S. Provisional Patent Application No. 63/323,286 (the “'286provisional”), filed Mar. 24, 2022, entitled “Floating SubstratesIncluding Carbonaceous Coatings for Offshore Cultivation of TargetProducts and Methods of Making and Using the Same;” U.S. ProvisionalPatent Application No. 63/393,381 (the “'381 provisional”), filed Jul.29, 2022, entitled “Systems and Methods for Sequestering Carbon DioxideUsing Alkaline Fluids;” and/or U.S. Provisional Patent Application No.63/401,959 (the “'959 provisional”), filed Aug. 29, 2022, entitled“Ocean Based Carbon Removal Systems and Methods of Using the Same,” thedisclosure of each of which is incorporated herein by reference in itsentirety.

For example, in some implementations, the cultivation apparatus 104 caninclude a first member (e.g., a buoy) configured to provide buoyancy atleast temporarily to various components of the cultivation apparatus104, a second member configured to cultivate and accumulate one or moretarget products (e.g., marine species), and/or, optionally, a releasecomponent to separate, disconnect, release and/or decouple the buoy fromthe member. In some instances, the first member or buoy of thecultivation apparatus 104 (referred to herein as first member) can alsobe configured to receive a species of target product (e.g., macroalgaegametophytes and/or sporophytes). In some implementations, the firstmember can be any suitable shape, size, and/or configuration. Forexample, in some embodiments, the first member can be a ring-like shape,triangular shape, disc, sphere, cylinder, cone, toroid, cuboid,polyhedral or any other geometrical shape. In some embodiments, thefirst member can be an irregular shape. In some embodiments, one or moreportions of the first member can be formed of a porous and/or hollowmaterial configured to provide buoyancy. In some embodiments, one ormore portions of the first member can be formed of a material relativelypermeable to oxygen, carbon dioxide, water, and water-soluble nutrientsto enable growth of target product. In some embodiments, one or moreportions of the first member can be formed of a relatively transparentmaterial configured to allow absorption of visible light.

Similarly, the second member is configured to cultivate and accumulateone or more species of the target product can be any suitable shape,size, and/or configuration. For example, in some embodiments, the secondmember can be a ring-like shape, triangular shape, disc, sphere,cylinder, cone, toroid, cuboid, polyhedral or any other geometricalshape. In some embodiments, the second member can be an irregular shape.In some embodiments, one or more portions of the second member can beformed of a porous and/or hollow material configured to providebuoyancy. In some embodiments, one or more portions of the second membercan be formed of a material relatively permeable to oxygen, carbondioxide, water, and water-soluble nutrients to enable target productgrowth. In some embodiments, one or more portions of the second membercan be formed of a relatively transparent material configured to allowabsorption of visible light. In some embodiments the shape, size, and/orconfiguration of the second member can be similar to or substantiallythe same as the shape, size, and/or configuration of the first member orbuoy. In other embodiments, the shape, size, and/or configuration of thesecond member can be different than the shape, size, and/orconfiguration of the first member or buoy.

The cultivation apparatus 104 can be used to seed one or more species ofa target product(s) that may be utilized in carbon sequestration. Forexample, in some instances, the first member of the cultivationapparatus 104 can be seeded with a target product species (e.g.,macroalgae gametophytes and/or sporophytes) that become positivelybuoyant as they mature, and the second member of the cultivationapparatus 104 can be seeded with a target product species (e.g.,macroalgae gametophytes and/or sporophytes) that become negativelybuoyant as they mature. In some embodiments, the first and secondmembers of the cultivation apparatus 104 can be seeded with positivelyand negatively buoyant target product(s), respectively, and thendeployed on oceans, lakes, rivers, and/or any other suitable body ofwater. The cultivation apparatus 104 can be further configured to bepositively buoyant when initially deployed on oceans, lakes, rivers,and/or any other suitable body of water. In some embodiments, thecultivation apparatus 104 can be configured to float for a predeterminedperiod of time after being deployed on oceans, lakes, rivers and/or anyother body of water, and then gradually sink as the second member seededwith negatively buoyant target product grows and obtains biomass.

While the cultivation apparatus 104 is described above as including thefirst member that is seeded with a target product that becomespositively buoyant as the target product matures, in otherimplementations, the first member can be seeded with a target productthat is and/or that becomes negatively buoyant as the target productmatures. In some implementations, for example, the first member and thesecond member can be seeded with the same target product or differenttarget products that each become negatively buoyant as the targetproducts mature.

In some implementations, regardless of buoyancy, the first and secondmembers of the cultivation apparatus 104 can be seeded with targetproducts based on any number of characteristics associated with thegrowth and/or accumulation of the target product. For example, in someimplementations, the first member can be seeded with a target productthat has a rate of growth that increases with and/or is otherwisepreferential to direct sunlight, while the second member can be seededwith a target product that has a rate of growth that would be slowedand/or that would be harmed if exposed to direct sunlight. Thus, in suchimplementations, the target product seeded on the first member can, forexample, receive a desired amount of direct sunlight while providingshade for the target product seeded on the second member. In someimplementations, the first member can be seeded with a target productthat can withstand rough surface conditions, while the second member canbe seeded with a target product that may, for example, have a fastergrowth rate or greater biomass accumulation but may be more fragile thanthe target product seeded on the first member. In a similar manner, anysuitable characteristics can be considered when determining the targetproducts seeded on the first and/or second member of the cultivationapparatus 104.

In some implementations, only the second member is seeded with a targetproduct while the first member acts, at least temporarily, as a buoy orthe like that is not seeded with a target product. For example, thefirst member can be a buoy or the like that can include and/or house anynumber of components, controllers, sensors, imaging devices,communication devices, radios, etc. configured to collect dataassociated with the cultivation apparatus 104 and/or an environment inwhich the cultivation apparatus is deployed (e.g., an area of theocean), to process, analyze, compress, condition, transform, etc. thecollected data, and/or to transmit the data to, for example, the server114 via the network 108, as described in further detail herein.

In some embodiments, the release component of the cultivation apparatus104 can be configured to degrade and/or mechanically separate,disconnect, detach, release and/or decouple from the first member and/orthe second member. For example, the release component can be configuredto detach, release, and/or decouple after a predetermined amount of timehas elapsed, after the selected species of target product has grownand/or obtained a predetermined amount of mass, and/or after a signal orgroup of signals operable to actuate the release component have beenreceived. In some implementations, the detaching, releasing, and/ordecoupling can allow the first member (and any target product attachedthereto) to float and the second member (and any target product attachedthereto) to sink. The first member can be then retrieved and/or reusedwhile the second member sinks to the bottom of the body of water (e.g.,ocean), which in turn, can sequester carbon dioxide captured by and/orassociated with the grown target product. In implementations in whichthe first member is seeded, the target product can be harvested and usedand/or sold for any suitable purpose. In some implementations, includingelectronic components (e.g., sensors, imaging devices, tracking devices,communication devices, compute devices, etc.) in or on the first memberthat is configured to float after being detached can allow thecomponents to be reused in another deployment. In some instances, thefirst member can be retrieved, and data associated with the cultivationapparatus 104 and/or the target product that is stored in a memorydevice or the like can be downloaded and/or retrieved.

While one implementation of the cultivation apparatus 104 is describedabove it should be understood that it is presented by way of exampleonly any not limitation. Other cultivation apparatus can be used wheredesirable. For example, in some embodiments, the cultivation apparatus104 can be and/or can include one or more portions forming one or moresubstrates, which may or may not be seeded, directly or indirectly, witha target product. In some implementations, the cultivation apparatus 104and/or portions or substrates thereof may be formed and/or sourced fromnaturally occurring materials, as described in the '285 provisional. Insome implementations, the cultivation apparatus 104 and/or portions orsubstrates thereof may be formed from and/or coated with one or morecarbonaceous coatings, alkaline minerals, and/or the like, as describedin the '286 provisional. In some implementations, the cultivationapparatus 104 and/or portions or substrates thereof can be formed usingand/or otherwise can be configured to release alkaline fluids and/or thelike, as described in the '381 provisional. In some implementations, thecultivation apparatus 104 and/or portions or substrates thereof can beconfigured to deliver and/or transport any suitable payload to a desiredarea of a body of water such as an ocean (e.g., a payload configured tosequester carbon or to facilitate the ocean's ability or capacity totransfer carbon from the fast carbon cycle (atmospheric CO2) to the slowcarbon cycle (deep ocean)), as described in the '959 provisional.

Each cultivation apparatus 104 can be coupled to, or associated with oneor more sensors 106 to sense, detect, measure, capture, and/or quantifyone or more characteristics and/or images relevant to the target productcultivated on the cultivation apparatus 104. In some embodiments, thesensors 106 can be mechanically coupled to the cultivation apparatus 104and/or a portion of the cultivation apparatus 104 (e.g., the positivelybuoyant first member or substrate). In some embodiments, the sensors 106can be electronically coupled to the cultivation apparatus 104 and/or aportion of the cultivation apparatus 104. In some embodiments, thesensors 106 can include one or more sensors configured to sense, detect,and/or measure water temperature, irradiance, dissolved oxygenconcentration, pH, concentration of nutrients, concentration ofdissolved carbon, salinity, plant size, plant density, and/or othercharacteristics related to target product growth, the cultivationapparatus 104, and/or the environment in which the cultivation apparatus104 is deployed.

In some embodiments, the sensors 106 can be included in or on a sensorbuoy, apparatus, and/or substrate, which may or may not be seeded withand/or otherwise directly used to cultivate a target product. In suchembodiments, one or more sensor buoys, apparatus, and/or substrates canbe included in a deployment of many cultivation apparatus 104 (e.g.,hundreds, thousands, tens of thousands, hundreds of thousands, millions,etc.) and can provide data associated with the deployment, targetproduct accumulation on one or more cultivation apparatus 104,environmental conditions, and/or any other suitable data. In someembodiments, the one or more sensors 106 can be similar to and/orsubstantially the same as any of the sensors described in the '243provisional. Similarly, any of the sensors 106 may be implemented and/orotherwise included in any suitable manner such as those described in the'243 provisional. Examples of sensors are provided below for context andare not intended to be limiting in any way. Other sensors or other typesof sensors may be used in addition to any of the sensors described belowor as an alternative to any of the sensors described below.

In some embodiments, the sensors 106 can include pressure-release depthsensors configured to measure, and/or record the sinking rate of one ormore portions of a cultivation apparatus 104. The pressure-release depthsensors can be configured to measure, and/or record the sinking rate asa function of time after the cultivation apparatus 104 is seeded withtarget product and deployed on oceans, lakes, rivers, and/or any othersuitable body of water. For example, the pressure-release depth sensorscan be configured to measure the sinking rate of the cultivationapparatus 104, decouple from the cultivation apparatus 104 once thecultivation apparatus 104 reaches a predetermined depth threshold,return to the surface, and emit the sinking rate information recordedvia satellite or other wireless communication (e.g., to the server 114via the network 108). In some instances, the sinking rate of thecultivation apparatus 104 can be used (e.g., by the server 108) toquantify the mass and related carbon captured and/or sequestered. Insome instances, the pressure-release depth sensors of the sensors 106can be used to determine whether the cultivation apparatus 104 has sunkbelow a predetermined depth or threshold associated with and/or suitablefor the permanent sequestration carbon.

In some embodiments, the sensors 106 can be configured to sense, detect,and/or monitor target product growth, mass generation, and/or mass yieldupon the cultivation apparatus 104 being seeded with target product, andbeing deployed on oceans, lakes, rivers, and/or any other suitable bodyof water. In some embodiments, the sensors 106 can include underwatercameras or other imaging technologies configured to image, record,and/or monitor any number of target products (e.g., plants and/orheterokonts like kelp, macroalgae, etc.), number of fronds per targetproduct, frond dimensions, and/or density associated to target productgrowth. For example, in some embodiments the sensors 106 can include astereoscopic camera system equipped with two or more lenses includingseparate image sensors to simulate human binocular vision and thusfacilitate obtaining images with perception of depth. In someembodiments, the stereoscopic camera system can be equipped with one ormore rectilinear lenses, fisheye lenses, and/or anamorphic lensesconfigured to produce detailed images of the target product growing onthe cultivation apparatus 104. In some embodiments, the stereoscopiccamera system can be configured to perform multiple image postprocessing steps. For example, in some embodiments, the stereoscopiccamera system can include a post processing step to analyze the imagesgenerated by the lenses and identify and/or correct distortions usingalgorithms that estimate distortion parameters and camera matrix throughthe use of, for example, a Levenberg-Marquardt solver and/or any othersuitable curve fitting methods. In some embodiments, the stereoscopiccamera system can include multiple post processing steps such as colorcorrection, brightness/contrast, sharpness, backscatter removal,cropping and the like. In some implementations, the post processingsteps can include analyzing the image data using computer vision and/orother machine learning techniques to determine characteristics of thetarget product represented in the image data. In some embodiments, thestereoscopic camera system can capture the raw image data and transmitthe data to the server 114, which in turn, can perform any of the postprocessing steps just described.

In some embodiments, the sensors 106 can also include cameras equippedwith Photosynthetically Active Radiation (PAR) sensors or otherirradiance measuring devices configured to measure photosynthetic lightlevels in air and water in the 400 to 761 nm range (or any othersuitable range of wavelength). The PAR sensors can be configured tomeasure photosynthetic photon flux density (PPFD) or the power ofelectromagnetic radiation in the visible light spectral range inmicromoles of photons per square meter per second. The data captured bythe PAR sensors or other devices can be used (e.g., by the server 114)to estimate, determine, and/or quantify the intensity of solar lightthat is available to the target product disposed on the cultivationapparatus 104 for photosynthesis, and thus estimate and/or infer therelative health of the target product and/or the rate of growth oftarget product as well as other marine organisms.

The images and/or image data captured and/or recorded by the cameras ofthe sensors 106 can be used to quantify and/or estimate, at least inpart, the mass accumulated on the cultivation apparatus 104, an amountof mass eroded from the cultivation apparatus 104 (e.g., allowed tonaturally break off and sink), and/or changes in the mass (e.g., rate ofmass accumulation). The images and/or image data can, for example,provide insights that facilitate evaluating the relative health of thetarget product. In some embodiments, the images and/or image datacaptured and/or recorded by the sensors 106 can be transmitted to aserver 114. In some instances, the images and/or image data capturedand/or recorded by the sensors 106 can be analyzed manually (e.g.,manual annotation by a user) to determine the amount of mass on thecultivation apparatus 104, the rate of growth of target product, and/orthe amount of CO₂ effectively captured by the mass accumulated on thecultivation apparatus 104. For example, in some embodiments, the sensors106 can initiate image capture (e.g., capture or record images and/orvideos of the target product attached to and/or otherwise associatedwith the cultivation apparatus 104 at different points in time), postprocess those images (e.g., adjust color, brightness/contrast,sharpness, backscatter removal, removal of noise, cropping and the like)and transmit the images and/or videos (e.g., to the server 114 via thenetwork 108) for data extraction or annotation by a user, andstatistical analysis of the extracted data. In other instances, theimages captured and/or recorded by the sensors 106 can be analyzed orannotated using computer vision algorithms (e.g., executed on or by theserver 114).

In some embodiments, the sensors 106 can include cameras equipped withan anti-fouling system configured to detect, prevent and/or minimize thedegradation of the various components of the sensors 106 due toaccumulation and/or growth of marine microorganisms, plants, algae, orsmall animals, as well as the microbiologically influenced corrosion(MIC) generated by metabolites of such marine microorganisms. In someembodiments, the anti-fouling system can include a detection lightsource such as a Light-Emitting-Diode (LED) lamp configured to direct abeam of light in the ultraviolet (250-280 nm) range to the lenses and/orother components of the underwater cameras and induce the emission offluorescence by the fluorophores of microorganisms, plants, algae,and/or small animals. The detection light source can be used to triggera fluorescence response to marine microorganisms deposited on thesensors 106, which can be detected by one or more cameras equipped withsuitable detectors such as a charge-coupled device (CCD), anelectron-multiplying charge coupled device (EM-CCD), and/or acomplementary metal oxide semiconductor (CMOS) detector. The cameras canquantify the intensity of a fluorescence signal that can be used toevaluate the accumulation of marine microorganisms on the sensors 106.In some instances, the detection light source can be used to remove atleast a fraction of the marine microorganisms accumulated on the sensors106 due to the microorganism's low tolerance to the frequency and/orwavelengths of UV radiation generated by the detection light source.

In some embodiments, the sensors 106 can include and/or can be one ormore tracking devices configured to produce, and/or transmit signalsassociated with a relative position of the cultivation apparatus 104upon (or after) being seeded with target product and deployed on oceans,estuaries, lakes, rivers, and/or any other suitable body of water. Theposition and/or trajectory of the cultivation apparatus 104 can betransmitted, recorded and/or stored (e.g., by the server 114) and can befurther employed by remote sensing devices to determine and/or quantify(directly or indirectly) target product growth, mass production, and/orcarbon capture. For example, in some instances, the cultivationapparatus 104 can include a Global Positioning System (GPS) trackingdevice configured to determine, record, and/or transmit the cultivationapparatus 104 geographic location. In other instances, the cultivationapparatus 104 can include Radio-Frequency Identification (RFID) devicesconfigured to determine, record, and/or transmit the cultivationapparatus 104 geographic and/or trajectory location. In some instances,trajectory data can be used (e.g., by or at the server 114) todetermine, calculate, and/or infer mass growth by comparing surface orsubsurface conditions (e.g., wind, current, etc.) with subsurface massmotion and/or the like.

In some embodiments, the one or more external data sources 110 canprovide information and/or data associated with the body of water (e.g.,ocean), weather, deployment of cultivation apparatus 104, etc. In someembodiments, the one or more external data sources 110 can include oceandata sources. Additionally or alternatively, the one or more externaldata sources 110 can include satellite data sources. In someembodiments, the ocean data and/or the satellite data can includemeasurements such as ocean surface temperatures, atmospheric temperatureand humidity, salinity of the water, color of the water, spectralreflection of the water, nutrient content, alkalinity, nitrogen content,water depth, wave sizes, wave periods, tide information, currentdirection, current speed, windage, relative position of the deployment102, dispersion of the deployment 102, density of the deployment 102,and/or the like. In some embodiments, ocean data and/or satellite datacan include data obtained from geostationary and/or polar-orbitingmeteorological spacecraft. Geostationary and polar-orbiting satellitescan provide data that are collected by ground stations. Nonlimitingand/or non-exhaustive examples of external data sources 110 can includeHYCOM data sources, the European Centre for Medium Range WeatherForecasts ERA5, ETOPO1 Bathymetry Data from the U.S. National Oceanicand Atmospheric Administration (NOAA), U.S. National Aeronautics andSpace Administration (NASA) and/or NOAA remote sensing databases, datasources providing benthic environment data, and/or the like.

In some embodiments, software onboard the one or more external datasources 110 may employ strategies to minimize the usage of costly andpower consuming satellite telemetry. These strategies may involve datacompression. They may involve data subset selection. They may involvethe use of machine learning models to subsample or summarize the data tobe transmitted. As such, it may be desirable to verify the data from theone or more external data sources 110 (e.g., via sensor data from theone or more sensors 106). In some embodiments, ocean data and/orsatellite data from the external data source(s) 110 can be calibratedwith ground truthing and used to quantify biomass production, biomassyield, and/or capacity for carbon capture. For example, surface orsubsurface conditions (e.g., ocean surface temperature) can becalibrated with temperature measurements from temperature sensors (e.g.,sensor 106) on a cultivation apparatus 104 to determine variancestherebetween. In some instances, the data from one or more external datasource(s) 110 (e.g., temperature data) can be smoothed and/or otherwisefit using corresponding data from the one or more sensors 106. Knowing avariance between the data collected by the external data source(s) 110and the data collected by the sensors 106 can, for example, increase anaccuracy associated with calculations and/or predictions that are madebased on that data. In some instances, calibrating and/or verifying thedata can allow inferences to be made associated with the trajectoryand/or dispersion of the cultivation apparatus 104 in the deployment102. Data associated with the trajectory and/or dispersion can then beused to inform, predict, and/or quantify biomass production, biomassyield, capacity for carbon capture, and/or the like. In someembodiments, ocean data and/or satellite data can be used for and/or canotherwise inform decision making processes such as determining initialparameters of one or more modeling algorithms and/or processes such asany of those described herein.

The data associated with the sensors 106 and the one or more externaldata sources 110 can be transmitted to the server 114 via network 108.The network 108 can be, for example, a digital telecommunication networkof servers (e.g., server 114). The server 114 and/or the sensors 106 andthe one or more external data sources 110 on the network 108 can beconnected via one or more wired or wireless communication networks (notshown) to share resources such as, for example, data storage and/orcomputing power. The wired or wireless communication networks betweenserver 114 and/or the sensors 106 and the one or more external datasources 110 of the network 108 can include one or more communicationchannels, for example, a radio frequency (RF) communication channel(s),a fiber optic communication channel(s), an electronic communicationchannel(s), and/or the like. The network 108 can be and/or include, forexample, the Internet, an intranet, a local area network (LAN), virtuallocal area network (VLAN), and/or the like or combinations thereof.

In some embodiments, data associated with the sensors 106 and the one ormore external data sources 110 can be transmitted to the server 114. Theserver 114 can analyze the received data to determine, calculate, model,predict, estimate, evaluate, etc. target product growth, quantify massproduction, and/or mass yield. In other words, data output by thesensors 106 and the one or more external data sources 110 can beanalyzed to determine target product growth, mass production, carboncapture and/or sequestration rates, quantities, or capacities, and/orthe like, as further described herein. For example, in some embodiments,the data from the sensors 106 can be used to verify and/or truth datafrom the one or more external data sources 110. For example, satellitedata with ocean surface temperature measurement can be compared totemperature measurement data from a sensor on a cultivation apparatus104. If the comparison yields a consistent difference between the twomeasurements that is constant, the models described herein can beparametrized by accounting for the difference. This can improve theaccuracy of the models. In some embodiments, data from the external datasources 110 can be used to verify and/or truth the data from the sensors106.

FIG. 1B is a schematic illustration of the server 114 included in thesystem of FIG. 1A. In some embodiments, the server 114 can include oneor more servers and/or one or more processors running on a cloudplatform (e.g., Microsoft Azure®, Amazon® web services, IBM® cloudcomputing, etc.). Generally, the server 114 described here may processdata and/or other signals to quantify, verify, predict, and/or infercharacteristics relating to the target product, cultivation apparatus,water body, deployment and/or the like for the purposes of carbonsequestration. The server 114 may be configured to receive, process,compile, compute, store, access, read, write, and/or transmit dataand/or other signals. In some embodiments, the server 114 can beconfigured to access or receive data and/or other signals from one ormore of a sensor and a storage medium (e.g., memory, flash drive, memorycard).

In some embodiments, the server 114 can include at least a processor114A, a memory 114B, and a communications device 114C. The processor114A can be any suitable processing device(s) configured to run and/orexecute a set of instructions or code. For example, the processor 114Acan be and/or can include one or more data processors, image processors,graphics processing units (GPU), physics processing units, digitalsignal processors (DSP), analog signal processors, mixed-signalprocessors, machine learning processors, deep learning processors,finite state machines (FSM), compression processors (e.g., datacompression to reduce data rate and/or memory requirements), encryptionprocessors (e.g., for secure wireless data and/or power transfer),and/or the like. The processor 114A can be, for example, ageneral-purpose processor, central processing unit (CPU),microprocessor, microcontroller, Field Programmable Gate Array (FPGA),an Application Specific Integrated Circuit (ASIC), a processor board, avirtual processor, and/or the like. The processor 114A can be configuredto run and/or execute application processes and/or other modules,processes and/or functions associated with the system 100. Theunderlying device technologies may be provided in a variety of componenttypes (e.g., metal-oxide semiconductor field-effect transistor (MOSFET)technologies like complementary metal-oxide semiconductor (CMOS),bipolar technologies like generative adversarial network (GAN), polymertechnologies (e.g., silicon-conjugated polymer and metal-conjugatedpolymer-metal structures), mixed analog and digital, and/or the like.

The memory 114B can be any suitable memory device(s) configured to storedata, information, computer code or instructions (such as thosedescribed above), and/or the like. In some embodiments, the memory 114Bcan be and/or can include one or more of a random access memory (RAM),static RAM (SRAM), dynamic RAM (DRAM), a memory buffer, an erasableprogrammable read-only memory (EPROM), an electrically erasableread-only memory (EEPROM), a read-only memory (ROM), flash memory,volatile memory, non-volatile memory, combinations thereof, and thelike. In some embodiments, the memory 114B can store instructions tocause the processor 114A to execute modules, processes, and/or functionsassociated with the system 100, such as training probabilistic models,executing models, aggregating the models, etc.

The communication device 114C can be any suitable device(s) and/orinterface(s) that can communicate with the network 108 (e.g., any or thedevices, sensors, and/or data sources described above, and/or anycombination or part thereof). Moreover, the communication device 114Ccan include one or more wired and/or wireless interfaces, such as, forexample, Ethernet interfaces, optical carrier (OC) interfaces, and/orasynchronous transfer mode (ATM) interfaces. In some embodiments, thecommunication device 114C can be, for example, a network interface cardand/or the like that can include at least an Ethernet port and/or awireless radio (e.g., a WiFi® radio, a Bluetooth® radio, etc.). In someembodiments, the communications device 114C can include one or moresatellite antenna. In some embodiments, the communications device 114Ccan be communicably coupled to an external device that includes one ormore satellite antenna, or a power source such as a battery or a solarpanel. In some embodiments, the communications device 114C can beconfigured to (1) read one or more characteristics relevant to thetarget product, (2) transmit signals representative of the cultivationapparatus, deployment, and/or the target product characteristics to oneor more external devices, and/or (3) receive from one or more externaldevices signals operable to control the sensors (e.g., sensors 106 inFIG. 1A).

In some implementations, the server 114 can be configured to performprocesses and/or execute programs, algorithms, models, and/or the likeassociated with determining target product accumulation (and/or erosion)and, for example, a corresponding capacity for capturing andsequestering carbon dioxide. For example, FIG. 2 is a flowchartillustrating a method 200 of determining carbon dioxide offset credits,according to some embodiments. In some implementations, the method 200can determine and/or can be used to determine carbon dioxide offsetcredits using and/or otherwise associated with the system 100 describedabove with reference to FIGS. 1A and 1B. At 202, the method 200 includesobtaining sensor data associated with at least a portion of a deploymentfor cultivating a target product in a body of water (e.g., an ocean).The sensor data can be obtained and/or received by a server (e.g., theserver 114). The sensor data can include data from sensors (e.g., theone or more sensors 106), one or more external data sources (e.g., theone or more external data sources 110), and/or any other data source.For example, one or more cultivation apparatus (e.g., the cultivationapparatus 104) can include sensors such as GPS devices, cameras,environmental sensors, etc. such as any of those described above. Thesensor data can include data associated with the ocean such astemperature of ocean surface, salinity, nutrient availability, etc.,data from the satellites such as weather forecast, temperature,humidity, etc., and data from cultivation apparatus such as targetproduct growth, sinking rate of one or more portions of a cultivationapparatus, cultivation apparatus float time as a function of size and/orwave intensity, float time for trajectory sensors and/or the like,cultivation apparatus and/or substrate size distribution and/or behaviorat given depths, dispersion, water temperature, irradiance, dissolvedoxygen concentration, concentration of nutrients, concentration ofdissolved carbon, carbonate dissolution, salinity, plant size, plantdensity, mass yield, etc.

At 204, the method 200 includes execution of at least one model from anumber of models to generate an output. The output may predict at leastone characteristic associated with the target product, the deployment,and/or a portion of the ocean in which the deployment is disposed. Theat least one model can receive as inputs sensor data obtained at 202and, in some instances, one or more outputs of at least some models ofthe multiple models. The output of each of the multiple models canpredict either characteristic(s) associated with the target product(e.g., growth accumulation of the target product, mass yield, sinkingrate, etc.), characteristic(s) associated with cultivation apparatusand/or deployment (e.g., dispersion of the cultivation apparatus and/ordeployment, ocean depth, etc.), characteristic(s) associated with theenvironment in which the cultivation apparatus and/or the deployment aredeployed (e.g., characteristics associated with an ocean or portion ofthe ocean), and/or any other characteristic(s). At 206, the output of atleast one of the models at 204 is provided as input to a quantificationmodel.

At 208, the method 200 includes execution of the quantification model.The quantification model can be a combination and/or an aggregation ofmultiple models (e.g., probabilistic model, statistical model,predictive model, etc.). Execution of the quantification model canresult in the quantification model generating an output associated withtarget product accumulation (with or without considering erosion of thetarget product) and/or a predicted capacity of the target product tosequester carbon. Since the quantification model is or can be anaggregation of several models, the predicted capacity of the targetproduct to sequester carbon based on the output of the quantificationmodel can have greater accuracy than the predicted and/or inferredcapacity based on individual output of each individual model (e.g., eachof the models at 204).

At 210, the method 200 includes determining, calculating, and/orpredicting carbon dioxide offset credits based on the predicted capacityresulting from the output of the quantification model. For example, insome embodiments, an amount of carbon that can be sequestered per unitof target product can be calculated and sold in a carbon credit market(or any other suitable market) as a credit tied to and/or otherwiseassociated with the calculated capacity of a target product (or at leasta portion or amount thereof) to sequester that carbon.

Models

Any of the systems and/or methods described herein can be configured toperform processes and/or execute algorithm, models, and/or programsassociated with determining, calculating, inferring, and/or predictingtarget product growth, accumulation, and/or erosion and a correspondingcapacity of the target product to capture and sequester carbon dioxide.For example, the system 100 described above with reference to FIGS. 1Aand 1B can be used to cultivate a target product on any number ofcultivation apparatus 104. The deployment 102 including the cultivationapparatus 104 and the target products seeded thereon can be deployed ina desired body of water (e.g., an ocean). As the target product growsand/or accumulates biomass (and/or begins to naturally erode biomass)the one or more sensors 106 of the cultivation apparatus 104 can senseand/or capture data associated with the deployment 102, the cultivationapparatus 104, and/or the target product (e.g., growth, accumulation,and/or other characteristics). In addition, one or more external datasources 110 can provide data related to the environment where thedeployment 102 is located. The data from the one or more sensors 106and/or one or more external data sources 110 can be provided to and/orreceived by the server 114 or other compute device, which in turn, canexecute any number of analyses, processes, algorithms, programs, etc.such as, for example, the models described below (e.g., machine learningmodels, artificial intelligence models, neural networks, and/or thelike).

In some implementations, the data provided as input into the modelsdescribed herein can be associated with and/or classified into one of anumber of different data categories. The categories are related toand/or indicative of the information, measurements, etc. included in thedata. For example, as used herein, “in sample” can refer to data (e.g.,information, measurements, etc.) that facilitates and/or is otherwiseused in initial model building, initial parameter estimation ordetermination, model training or tuning, etc. In some instances, sampledata can be collected from a data source (e.g., sensors, external datasources, empirical observations and/or calculations, etc.) and a modelsuch as any of the models described herein can be trained and/orexecuted using the data to provide a fit for the data, a forecast orprediction based on the data, and/or any other output. As such, the datainput into the model is “in sample” data. The “in sample” data and/ormeasurements can be used for model fitting, updating, and/or informingthe parameters of a model. As used herein, a model using and/or trainedbased on “in sample” data is referred to as a “reinforced” model.

As used herein, “out of sample” can refer to data (e.g., information,measurements, etc.) provided to a model that is not included in thesample data used, for example, to train the model (i.e., the “in sample”data). For example, in sample data associated with a first cultivationapparatus can be used to train a reinforced model. The reinforced model,in turn, can provide “in sample” forecasting and/or predictionsassociated with the first cultivation apparatus. In some instances, thereinforced model can also be used to generate a forecast and/orprediction associated with a second cultivation apparatus different fromthe first cultivation apparatus. In this instance, data associated withthe second cultivation apparatus is referred to as “out of sample” data.The out of sample data associated with the second cultivation apparatusis input into the reinforced model, which in turn, can output an “out ofsample” forecast and/or prediction associated with the secondcultivation apparatus. As used herein, a model using “out of sample”data and and/or using an output of a reinforced model based at least inpart on out of sample data is referred to as an “aggregated” model. Assuch, in sample data can be, for example, “fed back” into a reinforcedmodel for training, fitting, updating, forecasting, and/or predictingbased on the in-sample data, while an output of the reinforced modelbased at least in part on out of sample data can be “fed forward” toand/or used as an input for an aggregated model, which in turn, canprovide a forecast and/or prediction associated with the out of sampledata.

In addition, in sample data and out of sample data can be “direct” dataor “proxy” data. As used herein, “direct” data (e.g., information,measurements, etc.) can refer to data that is obtained directly from thetarget product, cultivation apparatus, and/or deployment. In someimplementations, “direct” data can be data and/or information that isdetermined empirically based on direct measurements and/or observations(e.g., performed in a laboratory, which may or may not be on-site wherea cultivation apparatus or the like is deployed). As used herein,“proxy” data (e.g., information, measurements, etc.) can refer to datathat is obtained from one or more sensors and/or external data sources(e.g., any of the sensors 106 and/or external data sources 110,respectively, described above with reference to FIG. 1A), which may ormay not be associated with and/or received from a specific cultivationapparatus of interest. In some instances, “proxy” data can be used toinfer characteristics, information, etc. associated with, for example,the deployment 102 that can include large numbers of individualcultivation apparatus 104 (e.g., hundreds of thousands or more).

Reinforced Calibration Model

FIG. 3 is a flowchart illustrating a method and/or process 300 oftraining a reinforced calibration model 310, according to someembodiments. In some implementations, the reinforced calibration model310 can model, forecast, and/or predict the amount of growth of a targetproduct on a given cultivation apparatus (e.g., structurally and/orfunctionally similar to the cultivation apparatus 104 described abovewith reference to FIG. 1A). In some embodiments, the reinforcedcalibration model 310 can be an iteratively trained model. Thereinforced calibration model 310 can be trained using in samplecultivation apparatus proxy data 302 and in sample cultivation apparatusdirect data 304. In some embodiments, the in-sample data (e.g., insample cultivation apparatus proxy data, measurements, etc. and/or insample cultivation apparatus direct data, measurements, etc.) canfacilitate initial model building (e.g., initial building and/orgeneration of the reinforced calibration model 310) and can facilitateinitial parameter estimation and/or definition (e.g., initial parameterestimation and/or definition for the reinforced calibration model 310).

The in-sample cultivation apparatus direct data 304 can be data and/ormeasurements that are obtained directly from the target product. Forexample, the target product can be extracted from the cultivationapparatus and can be analyzed (e.g., analyzing the tissue of the targetproduct in a laboratory such as an on-site or mobile laboratory and/orthe like) to determine, for example, the carbon content for the targetproduct, the mass of the target product, the yield of the targetproduct, etc. The in-sample cultivation apparatus proxy data 302 caninclude data and/or measurements obtained from one or more sensors(e.g., any of the sensors 106 described above with reference to FIG. 1A)associated with the cultivation apparatus. For example, image data fromthe cultivation apparatus can be used to determine the mass of thetarget product, yield of the target product, area of the cultivationapparatus, etc., which in turn, can allow for and/or can inform adetermination and/or calculation of the carbon content of the targetproduct. Additionally or alternatively, the in-sample cultivationapparatus proxy data 302 can also include remote sensing data and/ormeasurements received from one or more external data sources (e.g., anyof the external data sources 110 described above with reference to FIG.1A).

In some embodiments, the output of the reinforced calibration model 310can be scored (e.g., scoring 306 shown in FIG. 3 ) in comparison to thein-sample cultivation apparatus direct data 304 (e.g., directmeasurements). This score can inform perturbations (e.g., noise,deviations, adjustments, and/or changes—usually small—configured toregulate and/or tune a machine learning model) made to the reinforcedcalibration model 310 during the next iteration. Perturbations canenable determining one or more changes to parameter values. For example,perturbations can be achieved through a stateful model generation stepwhich can be used to change parameter values of the reinforcedcalibration model 310. For instance, the reinforced calibration model310 can be a neural network such as, for example, a long short-termmemory (LSTM) model. The LSTM model can comprise gates that regulateaddition and removal of information to cell states. At every time step,the state of these gates can change. Accordingly, the perturbations(e.g., feedback cycle) can enable the change of parameter values of thereinforced calibration model 310 at every time step, as desired, therebychanging the state of the gates of the reinforced calibration model 310.This feedback cycle repeats until the reinforced calibration model 310output score converges to a target or threshold score (e.g., a desiredcriterion) or saturates.

In some embodiments, the score can be based at least in part onnon-parametric scoring. In some embodiments, the score can be based atleast in part on weighted scoring. In some embodiments, the scoring canbe such that weights can be assigned to nodes and/or units of thereinforced calibration model 310 based at least in part on a comparisonof the reinforced calibration model 310 output to the in-samplecultivation apparatus direct data 304 (e.g., error). Said another way,an error that is representative of the difference between the output ofthe reinforced calibration model 310 and the in-sample cultivationapparatus direct measurements 304 can be determined. The weights can beassigned to the nodes and/or units of the reinforced calibration model310 based on the error, thereby tuning and/or training the reinforcedcalibration model 310.

For example, an output of the reinforced calibration model 310 can beassigned a weight, score, cost, and/or a penalty (referred to herein as“weight”) based on the error. For instance, overestimation by thereinforced calibration model 310 can result in a first weight beingassigned to the output of the reinforced calibration model 310 andunderestimation by the reinforced calibration model 310 can result in asecond weight being assigned that is different from the first weight.Similarly, if the error is below a certain threshold amount orpercentage (e.g., 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%,15%, 20%, or more), the reinforced calibration model 310 can be assigneda predetermined weight that is based on the error. However, for allerrors above the threshold percentage, the reinforced calibration model310 can be assigned a constant or global weight related to all outputsof the reinforced calibration model. Similarly, if the output of thereinforced calibration model 310 is a false positive, the reinforcedcalibration model 310 can be assigned a weight that may be differentfrom a weight assigned if the output of the reinforced calibration model310 is a false negative. In this manner, scores associated with theoutput of the reinforced calibration model can be assigned, where ahigher or better score can be associated with a higher degree ofaccuracy or, for example, a higher degree of correlation between anoutput of the reinforced calibration model 310 and direct or empiricalobservation, data, and/or measurement.

In some implementations, the parameters of the reinforced calibrationmodel 310 can be updated based on the score associated with an output.For instance, as discussed above, the initial parameters for the modelcan be generated using in sample cultivation apparatus directmeasurements 304 during the model generation step 308. The parameterscan be updated at model generation step 308 with every iteration and/ortime step. In some embodiments, the reinforced calibration model 310 canbe a matrix transformation, a non-linear model, a feature model, aneural network, a combination thereof, and/or the like. The output ofthe reinforced calibration model 310 can be representative of the growthof the target product on a cultivation apparatus. In some embodiments,the reinforced calibration model 310 can predict any suitablecharacteristic associated with the target product such as mass of thetarget product, yield of the target product, carbon content of thetarget product, etc. In some embodiments, one or more of thesecharacteristics can be representative of the growth of the targetproduct and/or can be used to forecast, predict, infer, and/or otherwisedetermine (referred to herein for simplicity as “predict”) the growth ofthe target product.

Aggregated Field Observations

FIG. 4 is a flowchart illustrating a method and/or process 400 ofexecuting an aggregated field observations model 416, according to someembodiments. In some embodiments, out of sample cultivation apparatusproxy measurements 412 of one or more characteristics of the targetproduct (e.g., carbon content for the target product, mass of the targetproduct, yield of the target product, etc.) can be inputs to areinforced calibration model 410 (e.g., reinforced calibration model 310in FIG. 3 ). Said another way, the out of sample cultivation apparatusproxy measurements 412 are fed forward through the reinforcedcalibration model 410, which can be trained (based on in sample data),for example, using the process and/or method described above withreference to the reinforced calibration model 310. The output of thereinforced calibration model 410 can be an input to the aggregated fieldobservations model 416. The input of the aggregated field observationsmodel 416 can also include out of sample cultivation apparatus directdata 414 (e.g., data from a cultivation apparatus different from thecultivation apparatus providing the in-sample cultivation apparatus dataused by the reinforced calibration model 310). That is, the output ofthe reinforced calibration model 410 (using the out of samplecultivation apparatus proxy data 412—e.g., data received from one ormore sensors or other data sources) and the out of sample cultivationapparatus direct data 414 (e.g., empirical or direct measurements taken,for example, on site of a deployment, in a laboratory, and/or the like)can be aggregated and/or executed in or by the aggregated fieldobservations model 416. In some embodiments, the aggregation can beachieved through incorporation of out of sample cultivation apparatusdirect data 414 as input to the aggregated field observations model 416,and/or through parametric or non-parametric summarization of the out ofsample cultivation apparatus direct data 414 combined with the output ofthe reinforced calibration model 410.

As discussed above, the out of sample direct data 414 can be data and/ormeasurements that are obtained directly from the target product. Forexample, the target product can be extracted from the cultivationapparatus and can be analyzed (e.g., analyzing the tissue of the targetproduct) to determine the carbon content for the target product, themass of the target product, the yield of the target product, etc. Theout of sample cultivation apparatus proxy data 412 can include dataand/or measurements obtained from one or more sensors (e.g., any of thesensors 106 described above with reference to FIG. 1A) associated withthe cultivation apparatus and/or one or more remote sensing data sources(e.g., any of the external data sources 110 described above withreference to FIG. 1A). For example, image data from the cultivationapparatus can be used to determine the mass of the target product, areaof the cultivation apparatus, etc.

Since the aggregated field observations model 416 is executed using theoutput of the reinforced calibration model 410, an accuracy of aprediction output by (or determined or inferred based on the output of)the aggregated field observations model 416 can be greater than anaccuracy of a prediction output by (or determined or inferred based onthe output of) the reinforced calibration model 410. Accordingly, insome instances, the aggregated field observations model 416 can predictthe amount of growth of a target product on a cultivation apparatus withgreater accuracy than, for example, the reinforced calibration model 410alone. In some embodiments, the aggregated field observations model 416can predict any suitable characteristic associated with the targetproduct such as mass of the target product, yield of the target product,carbon content of the target product, etc. In some embodiments, one ormore of these characteristics can be representative of the growth of thetarget product. Accordingly, the aggregated field observations model 416can predict the characteristic associated with the target product withgreater accuracy than, for example, the reinforced calibration model 410alone.

While described above as providing a prediction associated with one ormore characteristics of the target product, in other instances, theaggregated field observations model 416 can be used to verify, quantify,forecast, and/or predict any suitable characteristic and/or parameterassociated with a target product, cultivation apparatus, deployment,and/or environmental condition of an area where the deployment islocated. In such instances, the data received, considered, and/or usedcan be limited to data associated with the desired characteristic and/orparameter. In some instances, for example, the aggregated fieldobservations model 416 can be used to verify, for example, proxy datareceived from one or more sensors and/or external data sources.

Reinforced Dispersion Model

FIG. 5 is a flowchart illustrating a method and/or process 500 oftraining a reinforced dispersion model 530, according to someembodiments. In some implementations, the reinforced dispersion model530 can predict and/or summarize the location and/or dynamics of thecultivation apparatus of the deployment or a subset of the deployment ina body of water (e.g., the ocean). In some embodiments, the reinforceddispersion model 530 can be one or more models configured to apply orexecute any number of equations of motion associated with the fluiddynamics of the surface ocean, such as LaGrangian or Eulerian driftingdynamics models and/or the like. In some embodiments, these models canbe parameterized to allow adjustment or perturbations for fluiddynamical factors such as, for example, drag of the cultivationapparatus, substrates, and/or any other drifting object(s);sub-resolution-scale turbulence; and/or the like. In some embodiments,the models may be implementations of numerical analysis and/or solutionsto equations of motions such as Runge-Kutta methods, and/or the like. Insome embodiments, the models may be aided by the online assimilation ofempirical data and/or aided by a parametric or non-parametricinterpolation such as exponential smoothing, kriging, and/or the like.

In some implementations, the reinforced dispersion model 530 can modeland/or predict destruction or alteration of a cultivation apparatus,substrate, and/or any other drifting object by degradation ordissolution in the ocean. In such implementations, the reinforceddispersion model 530 may be and/or may incorporate a cumulative damagemodel based on, for example, Miner's Rule, and/or the like, in whichocean data 518 such as wave energy caused by windage, temperature,and/or local saturation states of solutes participating in dissolutionis used to model and/or predict damage, failure, fatigue, etc. of atleast a portion of a cultivation apparatus, substrate, and/or any otherdrifting object.

For example, the reinforced dispersion model 530 can infer thegeographical dispersion of an entire deployment. The geographicaldispersion of the entire deployment can be used to predict and/orforecast the trajectories for individual cultivation apparatus and/orthe population dynamics of the cultivation apparatus 104 before they arereleased into the ocean. The geographical dispersion of the cultivationapparatus can be used to infer product growth, and quantify massproduction, mass yield, carbon capture, etc. For example, thegeographical dispersion of the cultivation apparatus can be used byremote sensing techniques (e.g., near-infrared aerial photography, SPOTmultispectral imagery, aerial digital multispectral imaging systems(DMSC), and/or the like) to further determine product growth, andquantify mass production, mass yield, and carbon capture. In someembodiments, the trajectory data can be used to determine, calculate,and/or infer mass growth or can be used to inform such determinations,calculations, and/or inferences, for example, by comparing surface orsubsurface conditions (e.g., wind, current, etc.) with subsurface massmotion and/or the like.

In some embodiments, the reinforced dispersion model 530 can be aniteratively trained model. The reinforced dispersion model 530 can betrained using in sample cultivation apparatus location data 520 andocean data 518. As discussed above, the in-sample data and/ormeasurements (e.g., in sample cultivation apparatus location data 520)can facilitate initial model building (e.g., initial building and/orgeneration of the reinforced dispersion model 530) and can facilitateinitial parameter estimation (e.g., initial parameter estimation for thereinforced dispersion model 530). For instance, model generation at 524can include generating the model and the initial parameter estimationsusing in sample cultivation apparatus location data 520. The in-samplecultivation apparatus location data 520 can be measurements obtainedfrom one or more sensors (e.g., any of the sensors 106 described abovewith reference to FIG. 1A) associated with the cultivation apparatus.For example, in sample cultivation apparatus location data 520 can beobtained from a GPS tracking device and/or an RFID device integratedwith, coupled to, and/or associated with the cultivation apparatus. Theocean data 518 can be remotely sensed (e.g., satellite or dronemeasured) or can be in situ data. Ocean data 518 can include surfacetemperatures, atmospheric temperature and humidity, salinity of the bodyof water, color of the ocean, nutrient content, alkalinity, nitrogencontent, wave sizes, wave periods, tide information, current direction,current speed, windage, and/or the like. In some embodiments, the oceandata 518 can include data obtained from one or more external datasources (e.g., any of the external data source(s) 110 described abovewith reference to FIG. 1A).

In some embodiments, transformations 528 can be made to the input data(e.g., the ocean data 518), for example, to change the dimensionality ofthe reinforced dispersion model 530, or to summarize the input data inspace, in time, in ocean depth, or in metrics conjugate or derivative tothe primary data (e.g., in variance, rate of change, etc.). In someembodiments, transformations 528 can be made to the ocean data 518 inorder to reduce the noise in the data. For instance, transformations 528can reduce the random variations in the ocean data 518 and smooth theocean data 518 so that it can be used as input to the reinforceddispersion model 530. For example, temperature measurements of the oceansurface can include random variations. Consider an uncharacteristicallywarm day in January which introduces random variation and/or noise totemperature measurements obtained for January. Performingtransformations 528 to such measurements such as by curve fitting,applying exponential moving average, random walk, etc. can smooth thedata and eliminate the noise in the data. In some embodiments, theoutput of the reinforced dispersion model 530 can be scored (e.g.,scoring 522) in comparison to the transformed input data. This score caninform perturbations made to the reinforced dispersion model 530 duringthe next iteration. Perturbations can allow changes to parameter values.For example, perturbations can be achieved through a stateful modelgeneration step which can be used to change parameter values of thereinforced dispersion model 530, as described above with reference tothe reinforced calibration model 310. This feedback and/or iterativecycle repeats until the reinforced dispersion model 530 output scoreconverges to a target or threshold score or saturates.

Similar to the reinforced calibration model 310 discussed with referenceto FIG. 3 , in some embodiments, the score can be based onnon-parametric scoring. In some embodiments, the score can be based onweighted scoring. In some embodiments, the scoring can be such thatweights can be assigned based on a comparison of the reinforceddispersion model 530 output to the in-sample cultivation apparatuslocation data 520 (e.g., error). Said another way, an error that isrepresentative of the difference between the output of the reinforceddispersion model 530 and the in-sample cultivation apparatus locationdata 520 can be determined. The weights can be assigned based on thiserror, as described above with reference to the reinforced calibrationmodel 310. For example, the weights can be assigned based on anoverestimation and/or underestimation by the reinforced dispersion model530, whether the error exceeds a threshold percentage, and whether theoutput of the reinforced dispersion model 530 is false positive or afalse negative, etc. The parameter for the reinforced dispersion model530 can be updated based on the scoring. In some embodiments, modelgeneration 524 (e.g., initial model building and/or model generation ofthe reinforced dispersion model 530) can include generating the modelfrom existing ocean dispersion model created for academic, historical,and/or record-keeping purposes (e.g., an academic dispersion model 526).The output of the reinforced dispersion model 530 can be representativeof the geographical dispersion and/or the trajectory of the entiredeployment.

Aggregated Population Dynamics

FIG. 6 is a flowchart illustrating a method and/or process 600 ofexecuting an aggregated population dynamics model 636, according to someembodiments. In some embodiments, out of sample ocean data 632 can beinputs to a reinforced dispersion model 630 (e.g., the reinforceddispersion model 530 shown in FIG. 5 ). Said another way, the out ofsample ocean data 632 are fed forward through the trained reinforceddispersion model 630, which can be trained (based on in sample data),for example, using the process and/or method described above withreference to the reinforced dispersion model 530. The output of thereinforced dispersion model 630 can be an input to the aggregatedpopulation dynamics model 636. The input of the aggregated populationdynamics model 636 can also include out of sample cultivation apparatuslocation data 634. That is, the output of the reinforced dispersionmodel 630 and the out of sample cultivation apparatus location data 634can be aggregated and/or executed in or by the aggregated populationdynamics model 636. In some embodiments, the reinforced dispersion model630 can be aided by online assimilation of the out of sample data 634 asa step in the process of producing and/or defining the aggregatedpopulation dynamics model 636 (or an output thereof). In someembodiments, the aggregation can be achieved through incorporation ofout of sample cultivation apparatus location data 634 as input to theaggregated population dynamics model 636, and/or through parametric ornon-parametric summarization of the out of sample cultivation apparatuslocation data 634 combined with the output of the reinforced dispersionmodel 630.

As discussed above, out of sample cultivation apparatus location data634 can be data and/or measurements obtained from one or more sensorsassociated with the cultivation apparatus. For example, out of samplecultivation apparatus location data 634 can be obtained from a GPStracking device and/or an RFID device integrated with, coupled to,and/or associated with the cultivation apparatus. The out of sampleocean data 632 can be remotely sensed (e.g., satellite or dronemeasured) or can be in situ data. In some implementations, the out ofsample cultivation apparatus location data 634 and/or the out of sampleocean data 632 can be remotely sensed and/or can include data receivedfrom one or more external data sources (e.g., the external data sources110). Since the aggregated population dynamics model 636 is executedusing the output of the reinforced dispersion model 630, an accuracy ofa prediction output by (or determined or inferred based on the outputof) the aggregated population dynamics model 636 can be greater than anaccuracy of a prediction output by (or determined or inferred based onthe output of) the reinforced dispersion model 630. Accordingly, in atleast some instances, the aggregated population dynamics model 636 canpredict the geographical dispersion and/or the trajectory of the entiredeployment with greater accuracy than, for example, the reinforceddispersion model 630 alone.

Aggregated Environmental Metric

FIG. 7 is a flowchart illustrating a method and/or process 700 ofexecuting an aggregated environmental metric model 740, according tosome embodiments. In some embodiments, the aggregated environmentalmetric model 740 can be generated from environmental survey data 738 andin situ cultivation apparatus environmental data 742. The in situcultivation apparatus data 742 and the environmental survey data 738 canbe aggregated through parametric and/or non-parametric mixing (e.g.,parametric and/or non-parametric summation).

The in situ cultivation apparatus data 742 can be data and/ormeasurements obtained from one or more sensors (e.g., any of the sensors106 described above with reference to FIG. 1A) associated with,integrated with, and/or coupled to the cultivation apparatus. Theenvironmental survey data 738 can be obtained from external data sources(e.g., any of the external data sources 110 described above withreference to FIG. 1A). For example, the environmental survey data 738can be obtained from satellite data sources such as, for example,geostationary and polar-orbiting satellites. The environmental surveydata 738 can include measurements and/or inferences such as temperature,ocean salinity, or ocean concentration of various chemical species,families of species, or atomic species for the entire ocean. However,the in situ cultivation apparatus environmental data 742 can includemeasurements and/or inferences such as temperature, ocean salinity, orocean concentration of various chemical species, families of species, oratomic species for the entire ocean, for the cultivation apparatus,and/or the area of the ocean close to the cultivation apparatus. Theaggregated environmental metric model 740 can predict and/or verifyenvironmental metrics such as temperature, ocean salinity, oceanalkalinity, ocean concentration etc. for the ocean (or at least aportion thereof) with low uncertainty. In some instances, the aggregatedenvironmental metric model 740 can predict and/or verify environmentalmetrics with greater accuracy and/or certainty than predictions,determinations, calculations, etc. based on environment survey data 738alone or the in situ cultivation apparatus data 742 alone.

Reinforced Growth Model

FIG. 8 is a flowchart illustrating a method and/or process 800 oftraining a reinforced growth model 850, according to some embodiments.In some embodiments, initial model generation 846 (e.g., initialbuilding and/or generation of the reinforced growth model 850) caninclude generating the model from existing academic growth models 842created for academic, historical, and/or record-keeping purposes. Insome embodiments, the academic growth models 842 can be any suitablemodel(s) configured to predict the total biomass, carbon containingbiomass, and/or tissue carbon content of an organism. In someembodiments, the academic growth models 842 can originate in and/or canbe based at least in part on a scientific study of algae or higherplants (i.e., a group of plants that have vascular tissues). Theacademic growth models 842 may execute, apply, and/or includedifferential equations, or several coupled partial differentialequations, tracking various internal biological states of an organismand its interaction with the environment. In some embodiments, theacademic growth models 842 may explicitly couple or associate the growthof an organism to its experienced environmental conditions, such as, forexample, temperature, light, seasonality, nutrient availability, and/orthe like. In some embodiments, the academic growth models 842 may coupleor associate the growth of an organism or a population of organisms tothe dynamics of a broader local ecology, such as, for example, “NPZmodels” which couple, analyze, model, and/or predict the dynamics and/orinterrelationships of Nitrogen, Phytoplankton, and Zooplankton in anenvironment and for a given time.

In some embodiments, the inputs to the reinforced growth model 850 caninclude a combination of outputs from an aggregated population dynamicmodel 836 (e.g., the aggregated population dynamic model 636 shown inFIG. 6 ) and outputs from an aggregated environmental metric model 840(e.g., the aggregated environmental metric model 740 shown in FIG. 7 ).The combination of the outputs from the aggregated population dynamicmodel 836 that is representative of the geographical dispersion of thedeployment and the outputs from the aggregated environmental metricmodel 840 that is representative of the environmental metrics for thedeployment can, in turn, be provided as an input to the reinforcedgrowth model 850 that is representative of an inference of targetproduct accumulation on the cultivation apparatus of the deployment inthe open ocean. More specifically, the target product accumulation ofthe deployment can be quantitatively modeled as a function of thegrowing conditions such as the geographical dispersion of the deploymentand the environmental metrics for the deployment. In someimplementations, the target product accumulation can account for and/orpredict an amount of biomass erosion for the deployment, which may bebased at least in part on the growing conditions, etc. (e.g., watercurrent conditions, temperatures, pH, salinity, nutrient level, etc.).

In some embodiments, the output of the reinforced growth model 850 canbe scored (e.g., scoring 844) and compared to the output of theaggregated field observations 816 (e.g., the aggregated fieldobservations 316 shown in FIG. 3 ). This score can inform perturbationsmade to the reinforced growth model 850 during the next iteration and/orduring the next model generation step 846. Perturbations can allowchanges to parameter values. For example, perturbations can be achievedthrough a stateful model generation step 846 which can be used to changeparameter values of the reinforced growth model 850, as described abovewith reference to the reinforced calibration model 310 and thereinforced dispersion model 530. This feedback and/or iterative cyclecan repeat until the reinforced growth model 850 output score convergesto a target or threshold score (e.g., a criterion) or saturates.

Similar to the reinforced calibration model 310 discussed shown in FIG.3 and the reinforced dispersion model 530 shown in FIG. 5 , in someembodiments, the score can be based on non-parametric scoring and/orweighted scoring. For example, in some embodiments, the scoring can besuch that weights can be assigned based on a comparison of thereinforced growth model 850 output to the aggregated field observations816 output (e.g., error). Said another way, an error that isrepresentative of the difference between the output of the reinforcedgrowth model 850 and the output of the aggregated field observations 816can be determined. The weights can be assigned based on this error, asdescribed above with reference to the reinforced calibration model 310and/or the reinforced dispersion model 530. For example, the weights canbe assigned based on an overestimation and/or underestimation by thereinforced growth model 850, whether the error exceeds a thresholdpercentage, and whether the output of the reinforced growth model isfalse positive or a false negative, etc. The parameter for thereinforced growth model 850 can be updated based on the scoring. Theoutput of the reinforced growth model 850 can be representative ofand/or can forecast, predict, and/or estimate the target productaccumulation for the deployment.

Aggregated Growth Response

FIG. 9 is a flowchart illustrating a method and/or process 900 ofexecuting an aggregated growth response model 951, according to someembodiments. In some embodiments, a combination of the outputs from anaggregated environmental metric model 940 (e.g., the aggregatedenvironmental metric models 740 (FIG. 7 ) and/or 840 (FIG. 8 )) and theoutputs from an aggregated population dynamics model 936 (e.g., theaggregated population dynamics models 636 (FIG. 6 ) and/or 836 (FIG. 8)) can be inputs to a reinforced growth model 950 (e.g., the reinforcedgrowth model 850 shown in FIG. 8 ). Said another way, a combination ofthe outputs from aggregated environmental metric model 940 and theaggregated population dynamics model 936 are fed forward through thereinforced growth model 950.

The output of the reinforced growth model 950 can be an input to theaggregated growth response model 951. The input of the aggregated growthresponse model 951 can also include output from an aggregated fieldobservations model 916 (e.g., the aggregated field observations models416 (FIG. 4 ) and/or 816 (FIG. 8 )). That is, the output from thereinforced growth model 950 and output from the aggregated fieldobservations model 916 can be aggregated and/or executed in or by theaggregated growth response model 951. In some embodiments, theaggregation can be achieved and/or implemented by adding, supplementing,aggregating, and/or otherwise incorporating the output from theaggregated field observations model 916 (and/or data associated with themodel 916) as input to the aggregated growth response model 951, and/orthrough online assimilation, parametric, and/or non-parametricinterpolation and/or summarization associated with and/or based on theoutput of the aggregated field observations model 916 (and/or dataassociated with the model 916) combined with the output from thereinforced growth model 950. In some implementations, the output of theaggregated field observations model 916 (and/or data associated with themodel 916) may be direct measurements such as mass, surface area, carboncontent, and/or the like. In some implementations, the output of theaggregated field observations model 916 (and/or data associated with themodel 916) may be image-derived estimations of mass, surface area,carbon content, and the like achieved through previously trained machineimage modeling (e.g., via the reinforced calibration model 310 and/orthe like).

The aggregated growth response model 951 can be executed to predicttarget product accumulation for the deployment. Since the aggregatedgrowth response model 951 is executed using the output from thereinforced growth model 950, an accuracy of a prediction output by (ordetermined or inferred based on the output of) the aggregated growthresponse model 951 can be greater than an accuracy of a predictionoutput by (or determined or inferred based on an output of) thereinforced growth model 950 alone. Accordingly, in at least someinstances, the aggregated growth response model 951 can used to predicttarget product accumulation for the deployment (with or withoutaccounting for a degree or amount of biomass erosion) with greateraccuracy than the reinforced growth model 950.

Carbon Credit Quantification

FIG. 10 is a flowchart illustrating a method and/or process 1000 ofexecuting a carbon credit quantification model 1055, according to someembodiments. In some implementations, the carbon credit quantificationmodel(s) 1055 can be established, defined, generated, etc. in accordancewith commercial demands in the marketplace to convert a calculation ofnet carbon removal into a quantity of marketable credits. In someimplementations, the carbon credit quantification model(s) 1055 canapply, define, determine, model, and/or predict a risk-based discountdue to analytical uncertainty, statistical variance, and/or the like. Insome embodiments, the carbon credit quantification model(s) 1055 canapply, implement, and/or execute a market-facing principle, such aston-year accounting, and/or the like. In some embodiments, the carboncredit quantification model(s) can generate and/or output separatequantities for credits of various “tranches” distinguished by risk,durability, co-benefits, and/or the like.

As discussed above and seen from FIG. 10 , the carbon creditquantification model 1055 can be generated from data output from acombination of multiple models such as a reinforced calibration model1010 (e.g., the trained reinforced calibration model 410 shown in FIG. 4), an aggregated field observations model 1016 (e.g., the aggregatedfield observations model 416 shown in FIG. 4 ), a reinforced dispersionmodel 1030 (e.g., the trained reinforced dispersion model 630 shown inFIG. 6 ), an aggregated population dynamics model 1036 (e.g., theaggregated population dynamics model 636 shown in FIG. 6 ), anaggregated environmental metric model 1040 (e.g., the aggregatedenvironmental metric model 740 shown in FIG. 7 ), a reinforced growthmodel 1050 (e.g., the trained reinforced growth model 950 shown in FIG.9 ), and an aggregated growth response model 1051 (e.g., the aggregatedgrowth response model 951 shown in FIG. 9 ).

For example, the out of sample cultivation apparatus proxy data 1012(e.g., the out of sample cultivation apparatus proxy data 402 shown inFIG. 4 ) can be fed forward to the reinforced calibration model 1010.The output from the reinforced calibration model 1010 can be aggregatedwith the out of sample cultivation apparatus direct data 1014 (e.g., theout of sample cultivation apparatus direct measurement 414 shown in FIG.4 ) when executing the aggregated field observations model 1016.Similarly, the ocean data 1018 (e.g., the ocean data 518 shown in FIG. 5) can be fed forward to the reinforced dispersion model 1030. Althoughnot shown, in some instances, one or more transformations can beperformed on the ocean data 1018 (e.g., processes to normalize,summarize, smooth, fit, etc. the data, as described above with referenceto the transformations 528 shown in FIG. 5 ). The output from thereinforced dispersion model 1030 can be aggregated with the out ofsample cultivation apparatus location data 1034 (e.g., the out of samplecultivation apparatus location data 634 shown in FIG. 6 ) when executingthe aggregated population dynamics model 1040. The environmental surveydata 1038 (e.g., the environmental survey data 738 shown in FIG. 7 ) canbe aggregated with the in situ cultivation apparatus environmental data1042 (e.g., the in situ cultivation apparatus environmental data 742shown in FIG. 7 ) when executing the aggregated environmental model1040.

The output from the aggregated population dynamics model 1036 can becombined with the output from the aggregated environmental metric model1040 and fed forward to the reinforced growth model 1050. The outputfrom the reinforced growth model 1050 can be aggregated with the outputfrom the aggregated field observations model 1016 when executing theaggregated growth response model 1051. Accordingly, since the aggregatedgrowth response model 1051 receives as input and/or is based on acombination of data from the reinforced calibration model 1010, theaggregated field observations model 1016, the reinforced dispersionmodel 1030, the aggregated population dynamics model 1036, theaggregated environmental metric model 1040, and the reinforced growthmodel 1050, an accuracy of a prediction by (or determined or inferredbased on the output of) the aggregated growth response model 1051 can begreater than an accuracy of the prediction by (or determined or inferredbased on the output of) each of these individual models alone.

As shown in FIG. 10 , the output from the aggregated growth responsemodel 1051 can be used to execute the carbon credit quantification model1055 (e.g., can be provided as input to the credit quantification model1055). Therefore, an accuracy of a prediction by (or determined orinferred based on the output of) the carbon credit quantification model1055 of an amount, quantity, certainty, value, etc. of carbon dioxideoffset credits for the deployment can be greater than an accuracy of aprediction by (or determined or inferred based on the output of) each ofthe individual models.

For example, the reinforced calibration model 1010 can generate anoutput associated with and/or used to predict accumulation and/orerosion of the target product for a cultivation apparatus, which inturn, can be used to infer the carbon dioxide offset credits associatedwith and/or attributed to the cultivation apparatus. As described above,however, the output from the reinforced calibration model 1010 isaggregated with and/or verified against the out of sample cultivationapparatus direct data and/or measurements 1014 when executing theaggregated field observations model 1016, thereby verifying, enhancing,and/or otherwise increasing a certainty of the output of the reinforcedcalibration model 1010. Accordingly, the aggregated field observationsmodel 1016 can predict accumulation and/or erosion of the target productfor a cultivation apparatus with greater accuracy and/or certainty thanthe reinforced calibration model 1010 alone. This in turn improves aprediction, inference, calculation, and/or determination (or aconfidence or certainty therewith) of the carbon dioxide offset creditsfor the cultivation apparatus.

The reinforced dispersion model 1030 can predict the dispersion of thedeployment, which in turn, can provide information and/or data used toinfer carbon dioxide offset credits associated with the deployment. Asdescribed above, however, the output from the reinforced dispersionmodel 1030 is aggregated with and/or verified against the out of samplecultivation apparatus location data 1034 when executing the aggregatedpopulation dynamics model 1036, thereby verifying, enhancing, and/orotherwise increasing a certainty of the output of the reinforceddispersion model. Accordingly, the aggregated population dynamics model1036 can predict the dispersion of the deployment with greater accuracyand/or certainty than the reinforced dispersion model 1030 alone. Thisin turn improves the prediction, inference, calculation, and/ordetermination (or confidence or certainty therewith) of the carbondioxide offset credits for the deployment. As described above, theoutput from the aggregated population dynamics model 1036 is combined oraggregated with and/or verified against the output from the aggregatedenvironmental metric model 1040 when feeding forward through thereinforced growth model 1050. As such, the output from the reinforcedgrowth model 1050 can have greater accuracy and/or certainty than anoutput resulting from the use of only one of the aggregated populationdynamics model 1036 or the aggregated environmental metric 1040.

As described above, the output of the reinforced growth model 1050 isaggregated with and/or verified against the output of the aggregatedfield observations model 1016 when executing the aggregated growthresponse model 1051, thereby verifying, enhancing, and/or otherwiseincreasing a certainty of the output of the reinforced growth model1050. Accordingly, the aggregated growth response model 1051 can predictan amount of accumulation of the target product with greater accuracyand/or certainty than a prediction from the reinforced growth model 1050alone or the aggregated field observations 1016 alone. This in turnimproves the prediction, inference, calculation, and/or determination(or a confidence or certainty therewith) of the carbon dioxide offsetcredits for the deployment.

Based at least in part on the output of the aggregated growth response1051, the carbon credit quantification model 1055 can executed tocalculate, determine, and/or map an amount and/or value of carboncredits for and/or associated with the deployment. Although not shown inFIG. 10 , in some implementations, the carbon credit quantificationmodel 1055 can receive (in addition to the output of the aggregatedgrowth response model 1051) information and/or data associated with thedeployment such as location data, ocean data, etc. In some instances,the data can be received from any suitable data sources such as thesensors and/or external data sources described herein. In someinstances, the data can be the ocean data 1018 (or a portion thereof)provided as input into the reinforced dispersion model 1030, theenvironmental survey data 1038 (or a portion thereof) provided as inputinto the aggregated environmental metric 1040, and/or any other dataprovided as input into the previous models. In some instances, the datacan be a subset of the ocean data 1018 and/or the environmental surveydata 1038 not used in or by the previous models. In some instances, thedata can be in addition to and/or different from the data (or subset(s)thereof) provided as input into the previous models. In some instances,the data associated with the deployment can include, for example,location of the deployment in the body of water (e.g., where thedeployment is in the ocean), rate(s) of the air/sea flux pulling carbonout of the air, ocean surface or deep water circulation data, oceantracer diffusion models, ocean chemistry, ocean depth, and/or the like.Accordingly, the carbon credit quantification model 1055 can map,correlate, aggregate, compare, verify, etc. this data with the dataassociated with the accumulation and/or erosion of the target productoutput by the aggregated growth response model 1051.

In some instances, the carbon credit quantification model 1055 can beconfigured to account for uncertainty and/or error stacking associatedwith the data provided to the aggregated growth response model 1051(and/or any other data). For example, in some instances, a value can beassigned for a given amount of carbon sequestered for a given time(e.g., a value per unit such as a value per ton, kiloton, megaton, etc.of carbon sequestered for a predetermined time such as 100 years, 500years, 1,000 years, or more). The output of the aggregated growthresponse model 1051 can predict and/or determine an amount of biomassaccumulation for a deployment and the output of the carbon creditquantification model 1055 can predict and/or determine an amount ofcarbon sequestered by the deployment and how long the carbon will besequestered (e.g., based at least in part on sunken depth, and/or thelike) with a known degree of uncertainty such as, for example, +/−0.1%,0.5%, 1.0%, 5%, 10%, or more or any percentage or fraction of a percenttherebetween. In some instances, a value or amount of credit associatedwith the carbon sequestered can be determined based on the amount ofbiomass accumulation minus, for example, an amount of accumulationassociated with and/or otherwise representing the uncertainty. In someinstances, a discount or decrease in a value or an amount of credit canbe assigned based on the degree or amount of uncertainty, where agreater amount of uncertainty results in a greater discount or reductionin value or amount of credit.

In some implementations, the degree of uncertainty can be associatedwith and/or can be at least partially a function of a degree of biomasserosion for a deployment. For example, in some implementations, biomasscan naturally break off from the target product attached to thecultivation apparatus and can begin to sink. The eroded biomass,however, may be difficult to quantify and it may be infeasible and/orimpracticable to confirm whether the eroded biomass has sunk.Accordingly, while a certain amount of biomass erosion can be predicted,determined, and/or otherwise accounted for, biomass erosion can, in someinstances, increase a degree of uncertainty associated with targetproduct accumulation for the deployment.

Additionally or alternatively, uncertainty and/or error stacking can beassociated with and/or determined and/or assigned based on, for example,one or more scores associated with an output of one or more models(e.g., as described above with reference to the reinforced calibrationmodel 310, the reinforced dispersion model 530, and/or the reinforcedgrowth model 850). For example, a score (e.g., a confidence score) canbe assigned for an output of each of the reinforced calibration model1010, the reinforced dispersion model 1030, and/or the reinforced growthmodel 1050. With the output of these models being fed forward to, forexample, the aggregated growth response model 1051, a confidence scoreand/or degree of uncertainty can be predicted and/or determined for theoutput of the aggregated growth response model 1051 (based at least inpart on the scores). Accordingly, the carbon credit quantification model1055 can account for the score and/or degree of uncertainty of theoutput of the aggregated growth response model 1051 and/or a scoreand/or degree of uncertainty associated with the output of any othermodel. In some instances, a higher score can be representative of agreater degree of certainty associated with an output, which in turn,can result in a greater value being assigned for a determined,estimated, and/or predicted amount of carbon sequestered by adeployment.

In some implementations, a value of carbon credits associated with thebiomass accumulated for a given deployment can be based at least in parton a depth of the ocean where the biomass is sunk. In suchimplementations, a shallower depth may result in a length of carbonstorage and/or sequestration that shorter than a length of carbonstorage and/or sequestration associated with a greater depth. As such, avalue of the carbon credits associated with the target product biomasssunk at the greater depth can be greater than a value of the carboncredits associated with substantially the same amount of target productbiomass sunk at the shallower depth.

In some instances, the process of providing inputs into the carboncredit quantification model 1055 can include one or more verificationand/or normalization steps, procedures, and/or checks, for example, atand/or by at least some of the models, the output of which is fedforward into the next model. In some implementations, verificationand/or normalization can ensure that the models are executed usingaccurate input data (and/or data have a normalized form or otherwisehaving a desired dimensionality), which in turn, results in a higheraccuracy of the output of a subsequent model in the sequence. In thismanner, the accuracy of a prediction, inference, calculation, and/ordetermination of an amount of target product biomass accumulation for adeployment (e.g., an output of the aggregated growth response model1051) can be greater than a prediction, inference, calculation, and/ordetermination that could otherwise be made based on each individualmodel. With an accurate prediction and/or determination of the amount ofaccumulation for the deployment, a prediction, inference, calculation,and/or determination of carbon dioxide offset credits associated withand/or attributable to the deployment can be made with greater accuracythan could otherwise be made using an estimation of the accumulationallowed by the output of any of the models individually. Accordingly, avalue of the carbon dioxide offset credits can be determined with adesired degree of confidence and/or an evaluation of the credits can bemade with less uncertainty. Similarly, in some implementations, the datafrom and/or associated with any of the outputs can be transformed,normalized, vectorized, summarized, and/or the like prior to being fedforward into the next model. Accordingly, the verification and/ortransformation of data output by a model can ensure that data with adesired degree of accuracy and/or in a desired format, etc. is fedforward to the next model.

FIG. 11 is a flowchart illustrating a method and/or process 1100 ofexecuting a carbon credit quantification model 1155, according to someembodiments. As discussed above and seen from FIG. 11 , the carboncredit quantification model 1155 can be configured to generate an outputfrom and/or based at least in part on data output from a combination ofmultiple models such as a reinforced calibration model 1110 (e.g., thetrained reinforced calibration model 410 shown in FIG. 4 ), anaggregated field observations model 1116 (e.g., the aggregated fieldobservations model 416 shown in FIG. 4 ), a reinforced dispersion model1130 (e.g., the trained reinforced dispersion model 630 shown in FIG. 6), an aggregated population dynamics model 1136 (e.g., the aggregatedpopulation dynamics model 636 shown in FIG. 6 ), an aggregatedenvironmental metric model 1140 (e.g., the aggregated environmentalmetric model 740 shown in FIG. 7 ), a reinforced intervention model1152, an aggregated intervention metric 1153, and an aggregatedperturbation response model 1154. However, while the method 1000 and/orprocess shown in FIG. 10 is described as predicting carbon dioxideoffset credits based on an amount or accumulation of a target productthat is sunk to the bottom of a body of water (e.g., an ocean floor),the method and/or process 1100 described below with reference to FIG. 11can be used to determine and/or predict carbon dioxide offset creditsbased on any suitable intervention or combination of interventions(including sinking target products to the ocean floor) designed and/orintended to capture and sequester carbon dioxide, and/or to otherwisetransfer carbon dioxide from a fast carbon cycle (e.g., atmosphericcarbon dioxide) to a slow carbon cycle (e.g., the deep ocean or thelike).

For example, as described in detail above, the out of sample cultivationapparatus proxy data 1112 (e.g., the out of sample cultivation apparatusproxy data 402 shown in FIG. 4 ) can be fed forward to the reinforcedcalibration model 1110. The output from the reinforced calibration model1110 can be aggregated with the out of sample cultivation apparatusdirect data 1114 (e.g., the out of sample cultivation apparatus directmeasurement 414 shown in FIG. 4 ) when executing the aggregated fieldobservations model 1116. Similarly, the ocean data 1118 (e.g., the oceandata 518 shown in FIG. 5 ) can be fed forward to the reinforceddispersion model 1130 after a transformation 1128 (e.g., optionalprocesses to normalize, summarize, smooth, fit, etc. the data, asdescribed above with reference to the transformations 528 shown in FIG.5 ) is performed. The output from the reinforced dispersion model 1130can be aggregated with the out of sample cultivation apparatus locationdata 1134 (e.g., the out of sample cultivation apparatus location data634 shown in FIG. 6 ) when executing the aggregated population dynamicsmodel 1140. The environmental survey data 1138 (e.g., the environmentalsurvey data 738 shown in FIG. 7 ) can be aggregated with the in-situcultivation apparatus environmental data 1142 (e.g., the in situcultivation apparatus environmental data 742 shown in FIG. 7 ) whenexecuting the aggregated environmental model 1140.

The output of the aggregated population dynamics 1136 can be combinedwith the output from the aggregated environmental metric model 1140 andfed forward to the reinforced intervention model 1152. The reinforcedintervention model 1152 is representative of a given carbon removalintervention's conditions, where the carbon removal intervention mayinclude the placement of cultivation apparatus and/or substrates (e.g.,passive drifters) into ocean currents, dissolution of a chemical payloadinto the surface ocean, cultivation of target product(s), and/or thelike or combinations thereof (e.g., such as any of the embodimentsdescribed in the '315 patent, the '243 provisional, the '285provisional, the '286 provisional, the '381 provisional, and/or the '959provisional). In other words, carbon removal intervention can refer to amethod, mode, process, and/or system used or executed to capture carbon.Interventions can include the cultivation of target product(s) thatcapture carbon via photosynthesis, the chemical reactions resulting froman interaction between a body of water (e.g., an ocean) and a chemicalpayload (e.g., a carbonate and/or alkaline mineral, fluid, etc.), anenhancement of the ocean's natural ability to capture atmospheric carbonsuch as, for example, enhancing ocean alkalinity, and/or any othersuitable intervention(s). Nonlimiting examples of such interventions aredescribed, for example, with reference to the ocean-based carbon removalplatforms, techniques, and/or processes in the '959 provisional.

The reinforced intervention model 1152 can take in a baseline state(e.g., the aggregated environmental metric model 1140), which mayinclude data associated with the carbonate chemistry of surfaceseawater, the nutrient concentration of surface seawater, and/or thelike. The baseline state may include remote sensing measurements such asmultispectral ocean color photography as a proxy for chlorophyll contentand therefore nutrient availability. The baseline state may include insitu measurements by sensors for species of organic and inorganicnitrogen, bioavailable iron, and/or phosphorus, and/or fluorometers toestablish chlorophyll content of a target product as a proxy fornutrient viability (e.g., as described in the '243 provisional). Inaddition or as an alternative, the in-situ measurements may includepartial pressure carbon dioxide (pCO₂), alkalinity, total dissolvedinorganic carbon, dissolved carbon dioxide (CO₂), bicarbonate ion (HCO₃⁻), and/or carbonate ion (CO₃ ²⁻) content of the water. The reinforcedinvention model 1152 may also receive as input and/or may be combinedwith data representing and/or associated with a dosing rate (e.g.,determined, predicted, and/or inferred from the output of the aggregatedpopulation dynamics 1136), which may include data resulting from oceantransport modeling to establish the spatial population dynamics ofcultivation apparatus and/or substrates (e.g., passive drifters), whichmay or may not be seeded with a target product), data resulting fromcumulative damage models, and/or observational information aboutdisaggregation and dissolution of a deployment as well as thecorresponding rate at which chemical payload material may be dissolvedinto seawater (e.g., via the cultivation apparatus and/or substratesdescribed in, for example, the '285 provisional, the '286 provisional,the '381 provisional, and/or the '959 provisional).

Although not shown, in some implementations, the reinforced interventionmodel 1152 can be trained in a manner similar to the trained reinforcedgrowth model 950 shown in FIG. 9 . The output from the reinforcedintervention model 1152 (e.g., after training) can be aggregated withthe aggregated field observations 1116 when executing the aggregatedintervention metric model 1153. Accordingly, since the aggregatedintervention metric model 1153 receives and/or is based on a combinationof data from the reinforced calibration model 1110, the aggregated fieldobservations model 1116, the reinforced dispersion model 1130, theaggregated population dynamics model 1136, the aggregated environmentalmetric model 1140, and/or the reinforced intervention model 1152, anaccuracy of a prediction by (or determined or inferred based on theoutput of) the aggregated intervention metric model 1152 can be greaterthan an accuracy of prediction by (or determined or inferred based onthe output of) each of these individual models alone.

The output of the aggregated intervention metric model 1153, as well asthe aggregated environmental metric model 1140 may be fed forward to theaggregated perturbation response model 1154. The aggregated perturbationresponse model 1154 is initialized to the baseline state, as describedin reference to the reinforced intervention model 1152. Accordingly, thebaseline state may include data associated with a body of water (e.g.,an ocean) in which the intervention is being performed as well as dataassociated with the intervention being performed or the intervention tobe performed, such as a magnitude of dosing resulting from a chemicalpayload, a predicted amount of target product accumulation, a dispersionand/or dissolution of cultivation apparatus and/or substrates in adeployment, and/or the like (e.g., from the aggregated populationdynamics 1136). The aggregated perturbation response model 1154 maytrack the perturbations resulting from the intervention(s) through thedynamic ocean system and may characterize the chemical and/orbiogeochemical rebalancing of ocean processing in the presence of oceanwater mixing and physical advection of ocean waves. Perturbationmodelling may quantify a drawdown of carbon from an atmospheric and/orhydrospheric carbon cycle to the deep ocean carbon cycle as a result ofa given perturbation. Perturbation modelling can in turn be used topredict and/or infer the carbon dioxide offset credits associated withand/or attributed to the intervention (e.g., a cultivation apparatusand/or substrate, a release of a chemical payload, and/or the like).Accordingly, since the aggregated perturbation response model 1154receives and/or is based on a combination of data from the reinforcedcalibration model 1110, the aggregated field observations model 1116,the reinforced dispersion model 1130, the aggregated population dynamicsmodel 1136, the aggregated environmental metric model 1140, thereinforced intervention model 1152, and the aggregated environmentalmetric 1140, an accuracy of a prediction by (or determined or inferredbased on the output of) the aggregated perturbation response model 1154can be greater than an accuracy of prediction by (or determined orinferred based on the output of) each of these individual models alone.

The output of the aggregated perturbation response model 1154 may be fedforward into the carbon credit quantification model 1155. The carboncredit quantification model 1155 can be executed to calculate,determine, and/or map an amount and/or value of carbon credits forand/or associated with the deployment and/or intervention. Although notshown in FIG. 11 , in some implementations, the carbon creditquantification model 1155 can receive (in addition to the output of theaggregated perturbation model 1154) information and/or data associatedwith the deployment such as, location data, ocean data, etc. In someinstances, the data can be received from any suitable data sources suchas the sensors and/or external data sources described herein. In someinstances, the data can be the ocean data 1118 (or a portion thereof)provided as input into the reinforced dispersion model 1130, theenvironmental survey data 1138 (or a portion thereof) provided as inputinto the aggregated environmental metric 1140, and/or any other dataprovided as input into the previous models. In some instances, the datacan be a subset of the ocean data 1118 and/or the environmental surveydata 1138 not used in or by the previous models. In some instances, thedata can be in addition to and/or different from the data (or subset(s)thereof) provided as input into the previous models.

In some instances, such as when the intervention is achieved throughseeding or stimulation of atrophic biomass target product (e.g.,macroalgae), the carbon credit quantification model 1155 maycharacterize certain characteristics. The characteristics may includethe baseline surface ocean chemistry, the rate and spatial distributionof biomass accumulation and resulting biomass accumulation includingcarbon uptake pathways from the surface waters, the fate ofphotosynthetically fixed biomass at the surface ocean and the rate ofdegradation at the surface or flux to the deep ocean (e.g., ambient orattached to or seeded on a cultivation apparatus and/or substrate),and/or the resulting perturbation to the earth system carbon cycle.

In some instances, the data associated with the deployment and/or othercarbon removal intervention can include, for example, location of thedeployment in the body of water (e.g., where the deployment is in theocean), rate(s) of the air/sea flux pulling carbon out of the air, oceansurface or deep water circulation data, ocean tracer diffusion models,ocean chemistry, ocean depth, and/or the like. Accordingly, the carboncredit quantification model 1155 can map, correlate, aggregate, etc.this data with the data associated with the accumulation and/or erosionof the target product output by aggregated intervention metric model1153 (e.g., performing a function and/or providing an output similar tothe aggregated growth response model 1051 described above with referenceto FIG. 10 ). For example, in some implementations, a value of carboncredits associated with the biomass accumulated for a given deploymentcan be based at least in part on a depth of the ocean where the biomassis sunk. In such implementations, a shallower depth may result in alength of carbon storage and/or sequestration that shorter than a lengthof carbon storage and/or sequestration associated with a greater depth.As such, a value of the carbon credits associated with the targetproduct biomass sunk at the greater depth can be greater than a value ofthe carbon credits associated with substantially the same amount oftarget product biomass sunk at the shallower depth.

In some instances, the carbon credit quantification model 1155 can beconfigured to account for uncertainty and/or error stacking associatedwith the data provided to the aggregated perturbation response model1154. For example, in some instances, a value can be assigned for agiven amount of carbon sequestered for a given time (e.g., a value perunit such as a value per ton, kiloton, megaton, etc. of carbonsequestered for a predetermined time such as 100 years, 500 years, 1,000years, or more). The output of the aggregated perturbation response 1154can predict and/or determine the effects of an intervention duringdeployment and the output of the carbon credit quantification model 1155can predict and/or determine an amount of carbon sequestered by thedeployment and how long the carbon will be sequestered (e.g., based atleast in part on sunken depth, and/or the like) with a known degree ofuncertainty such as, for example, +/−0.1%, 0.5%, 1.0%, 5%, 10%, or moreor any percentage or fraction of a percent therebetween. In someinstances, a value associated with the carbon sequestered can bedetermined based on the amount of biomass accumulation minus, forexample, an amount of accumulation associated with and/or otherwiserepresenting the uncertainty. In some instances, a discount or decreasein a value can be assigned based on the degree or amount of uncertainty,where a greater amount of uncertainty results in a greater discount orreduction in value.

In some implementations, the degree of uncertainty can be associatedwith and/or can be at least partially a function of a degree of biomasserosion for a deployment. For example, in some implementations, biomasscan naturally break off from the target product attached to thecultivation apparatus and can begin to sink. The eroded biomass,however, may be difficult to quantify and it may be infeasible toconfirm whether the eroded biomass has sunk. Accordingly, while acertain amount of biomass erosion can be predicted, determined, and/orotherwise accounted for, biomass erosion can, in some instances,increase a degree of uncertainty associated with target productaccumulation for the deployment.

Additionally or alternatively, uncertainty and/or error stacking can beassociated with and/or determined and/or assigned based on, for example,one or more scores associated with an output of one or more models(e.g., as described above with reference to the reinforced calibrationmodel 310, the reinforced dispersion model 530, and/or the reinforcedgrowth model 850). For example, a score (e.g., a confidence score) canbe assigned for an output of each of the reinforced calibration model1110, the reinforced dispersion model 1130, and/or the reinforcedintervention model 1152. With the output of these models being fedforward to, for example, the aggregated perturbation response model1154, a confidence score and/or degree of uncertainty can be predictedand/or determined for the output of the aggregated perturbation responsemodel 1154 (based at least in part on the scores). Accordingly, thecarbon credit quantification model 1155 can account for the score and/ordegree of uncertainty of the output of the aggregated perturbationresponse model 1154 and/or a score and/or degree of uncertaintyassociated with the output of any other model. In some instances, ahigher score can be representative of a greater degree of certaintyassociated with an output, which in turn, can result in a greater valuebeing assigned for a determined, estimated, and/or predicted amount ofcarbon sequestered by a deployment.

In some instances, the process of providing inputs into the carboncredit quantification model 1155 can include one or more verificationsteps, procedures, and/or checks, for example, at and/or by at leastsome of the models, the output for which is fed forward into the nextmodel. The verification can ensure that the models are executed usingaccurate input data, which in turn, results in a higher accuracy of theoutput of a subsequent model in the sequence. In this manner, theaccuracy of a prediction, inference, calculation, and/or determinationof an amount of target product biomass accumulation for a deployment(e.g., an output of the aggregated perturbation response model 1154)and/or a result of a given intervention and/or perturbation can begreater than a prediction, inference, calculation, and/or determinationthat could otherwise be made based on each individual model. With anaccurate prediction and/or determination of the result of anintervention (e.g., an amount of accumulation for a deployment ofcultivation apparatus and/or substrates, and/or the like), a prediction,inference, calculation, and/or determination of carbon dioxide offsetcredits associated with and/or attributable to the intervention (e.g.,the deployment) can be made with greater accuracy than could otherwisebe made using an estimation of the result of the intervention allowed bythe output of any of the models individually. Accordingly, a value ofthe carbon dioxide offset credits can be determined with a desireddegree of confidence and/or an evaluation of the credits can be madewith less uncertainty.

FIG. 12 is a flowchart of an example method 1200 for determiningaccumulation of target product in a deployment (e.g., the deployment 102described above with reference to FIG. 1 ), which in turn, can be usedto determine carbon dioxide offset credits, according to someembodiments. At 1202 the method can include obtaining sensor data fromone or more sensors (e.g., any of the sensors 106 described above withreference to FIG. 1 ) associated with a cultivation apparatus (e.g., thecultivation apparatus 104 described above with reference to FIG. 1 ).The sensor data can include in sample cultivation apparatus proxy dataand/or measurements, in sample cultivation apparatus direct data and/ormeasurements, and/or out of sample cultivation apparatus direct dataand/or measurements. At 1204, the method 1200 can include training amodel based on the sensor data obtained at 1202. For instance, the modelcan be a reinforced calibration model and/or an aggregated fieldobservations model. The output of the model can predict a parameter thatcan be associated with and/or that can be used in determining and/orpredicting a growth of the target product of the cultivation apparatus.For instance, the output of the model can be representative of theamount of growth of a target product on a single cultivation apparatus.At 1206, the method 1200 can include obtaining sensor data from one ormore sensors associated with a deployment. The sensors can be sensorsassociated with each individual cultivation apparatus and/or externaldata sources (e.g., any of the external data source(s) 110 describedabove with reference to FIG. 1 ) such as one or more ocean sensor datasources and/or one or more satellite data sources that can obtain datafor and/or associated with the entire deployment. At 1208, the methodcan include training a second model based on the sensor data obtained at1206. For instance, the model can be a reinforced dispersion modeland/or an aggregated population dynamics model. The output of the modelcan, for example, predict the geographic dispersion of the deployment inthe ocean. At 1210, the method 1200 can include training a third modelbased on the output from the first model trained at 1204 and the outputfrom the second model trained at 1208. The third model can be areinforced growth model and/or an aggregated growth response model. Theoutput of this model can, for example, predict an amount of accumulationof the target product for all cultivation apparatus of the deployment.In some implementations, the output of the third model, optionally, canbe used to execute a carbon quantification model that can be then usedto determine the carbon dioxide offset credits for the deployment. Asdiscussed above, the accuracy of prediction by the third model can begreater than the accuracy of prediction by the first model or by thesecond model.

Some embodiments described herein relate to a computer storage productwith a non-transitory computer-readable medium (also can be referred toas a non-transitory processor-readable medium) having instructions orcomputer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) may bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to, magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asApplication-Specific Integrated Circuits (ASICs), Programmable LogicDevices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM)devices. Other embodiments described herein relate to a computer programproduct, which can include, for example, the instructions and/orcomputer code discussed herein.

Some embodiments and/or methods described herein can be performed bysoftware (executed on hardware), hardware, or a combination thereof.Hardware modules may include, for example, a general-purpose processor,an FPGA, an ASIC, and/or the like. Software modules (executed onhardware) can be expressed in a variety of software languages (e.g.,computer code), including C, C++, Java™, Ruby, Visual Basic™, Python™,and/or other object-oriented, procedural, or other programming languageand development tools. Examples of computer code include, but are notlimited to, micro-code or micro-instructions, machine instructions, suchas produced by a compiler, code used to produce a web service, and filescontaining higher-level instructions that are executed by a computerusing an interpreter. For example, embodiments may be implemented usingimperative programming languages (e.g., C, Fortran, etc.), functionalprogramming languages (Haskell, Erlang, etc.), logical programminglanguages (e.g., Prolog), object-oriented programming languages (e.g.,Java, C++, etc.) or other suitable programming languages and/ordevelopment tools, and/or combinations thereof (e.g., Python™).Additional examples of computer code include, but are not limited to,control signals, encrypted code, and compressed code.

While various embodiments have been particularly shown and described, itshould be understood that they have been presented by way of exampleonly, and not limitation. Various changes in form and/or detail may bemade without departing from the spirit of the disclosure and/or withoutaltering the function and/or advantages thereof unless expressly statedotherwise. Where schematics and/or embodiments described above indicatecertain components arranged in certain orientations or positions, thearrangement of components may be modified.

Although various embodiments have been described as having particularfeatures and/or combinations of components, other embodiments arepossible having a combination of any features and/or components from anyof embodiments described herein, except mutually exclusive combinations.The embodiments described herein can include various combinations and/orsub-combinations of the functions, components, and/or features of thedifferent embodiments described.

The specific configurations of the various components can also bevaried. For example, the size and specific shape of the variouscomponents can be different from the embodiments shown, while stillproviding the functions as described herein. More specifically, the sizeand shape of the various components can be specifically selected for adesired or intended usage. Thus, it should be understood that the size,shape, and/or arrangement of the embodiments and/or components thereofcan be adapted for a given use unless the context explicitly statesotherwise.

Where methods and/or events described above indicate certain eventsand/or procedures occurring in certain order, the ordering of certainevents and/or procedures may be modified. Additionally, certain eventsand/or procedures may be performed concurrently in a parallel processwhen possible, as well as performed sequentially as described above.

What is claimed:
 1. A method, comprising: obtaining sensor dataassociated with at least a portion of a deployment for cultivating atarget product in a body of water; executing at least one model from aplurality of models based at least in part on the sensor data togenerate an output predicting at least one characteristic associatedwith the target product, the deployment, or a portion of the body ofwater in which the deployment is disposed, providing as input to aquantification model the output of the at least one model from theplurality of models; executing the quantification model to generate anoutput associated with a predicted capacity of the target product of thedeployment to sequester carbon dioxide, an accuracy of the predictedcapacity resulting from the output of the quantification model beinggreater than an accuracy of a predicted capacity resulting from theindividual output of each model from the plurality of models; anddetermining carbon dioxide offset credits based on the predictedcapacity resulting from the output of the quantification model.
 2. Themethod of claim 1, wherein the sensor data includes data from at leastone sensor deployed on at least the portion of the deployment, data fromat least one data source associated with the body of water, or data fromat least one satellite data source.
 3. The method of claim 1, wherein atleast the portion of the deployment includes a cultivation apparatus. 4.The method of claim 1, wherein the plurality of models includes at leasta first model and a second model, the method further comprising:aggregating data associated with direct measurements of at least onecharacteristic of the target product and a first output from the firstmodel, the first output predicting at least one parameter associatedwith growth of the target product; and executing, based at least in parton the aggregation, the second model to generate a second output, thesecond output predicting growth of the target product.
 5. The method ofclaim 4, wherein an accuracy of the predicted growth resulting from thesecond output is greater than an accuracy of a predicted growthresulting from the first output.
 6. The method of claim 1, wherein theplurality of models includes at least a first model, a second model, athird model, and a fourth model, the method further comprising:executing a first aggregation of a first output from the first model anddata associated with direct measurements of at least one characteristicof the target product, the first output predicting at least oneparameter associated with growth of the target product; executing, basedat least in part on the first aggregation, the second model to generatea second output, the second output predicting growth of the targetproduct; executing a second aggregation of a third output from the thirdmodel and data associated with direct measurements of at least onecharacteristic of at least the portion of the deployment, the thirdoutput predicting a geographic dispersion of at least the portion of thedeployment in the body of water; and executing, based at least in parton the second aggregation, the fourth model to generate a fourth output,the fourth output predicting a geographic dispersion of the deploymentin the body of water.
 7. The method of claim 6, wherein an accuracy ofthe predicted geographic dispersion of the deployment resulting from thefourth output is greater than an accuracy of the predicted geographicdispersion of the deployment resulting from the third output.
 8. Themethod of claim 1, wherein the body of water is an ocean, the pluralityof models includes at least a first model, a second model, a thirdmodel, a fourth model, a fifth model, and a sixth model, the methodfurther comprising: executing a first aggregation of a first output fromthe first model and data associated with direct measurements of at leastone characteristic of the target product, the first output predicting atleast one parameter associated with growth of the target product;executing, based at least in part on the first aggregation, the secondmodel to generate a second output, the second output predicting growthof the target product; executing a second aggregation of a third outputfrom the third model and data associated with direct measurements of atleast one characteristic of at least the portion of the deployment, thethird output predicting a geographic dispersion of at least the portionof the deployment in the ocean; and executing, based at least in part onthe second aggregation, the fourth model to generate a fourth output,the fourth output predicting a geographic dispersion of the deploymentin the ocean; executing a third aggregation of the second output and afifth output from the fifth model, the fifth output predicting an amountof accumulation of the target product based at least in part on thefourth output and data associated with at least one environmentalcharacteristic of a portion of the ocean in which the deployment isdeployed; and executing, based at least in part on the thirdaggregation, a sixth model to generate a sixth output, the sixth outputpredicting an amount of accumulation of the target product.
 9. Themethod of claim 8, wherein an accuracy of the predicted amount ofaccumulation of the target product resulting from the sixth output isgreater than and accuracy of each of (i) the predicted amount ofaccumulation of the target product resulting from the fifth output and(ii) a predicted amount of accumulation of the target product resultingfrom the second output.
 10. The method of claim 8, wherein thequantification model receives as an input the sixth output from thesixth model.
 11. A method, comprising: obtaining sensor data associatedwith at least a portion of a deployment for cultivating a target productin a body of water; providing at least a portion of the sensor data asinput to at least one model from a plurality of models associated withat least one of the target product, the deployment, or a portion of thebody of water in which the deployment is disposed; executing theplurality of models in a predetermined sequence such that an output of acurrent model is an input for at least one subsequently executed modelaccording to the predetermined sequence; providing as input to aquantification model an output of a last model from the plurality ofmodels according to the predetermined sequence; and executing thequantification model to generate an output associated with a predictedcapacity of the target product to sequester carbon dioxide.
 12. Themethod of claim 11, further comprising: determining carbon dioxideoffset credits based on the predicted capacity resulting from the outputof the quantification model.
 13. The method of claim 11, wherein anaccuracy of the predicted capacity resulting from the output of thequantification model is greater than an accuracy of a predicted capacityresulting from an output of each model from the plurality of models. 14.The method of claim 11, wherein the providing at least the portion ofthe sensor data as input to at least one model from the plurality ofmodels includes: providing a portion of the sensor data to a model fromthe plurality of models configured to generate an output predicting atleast one characteristic associated with the target product.
 15. Themethod of claim 11, wherein the providing at least the portion of thesensor data as input to at least one model from the plurality of modelsincludes: providing a first portion of the sensor data to a first modelfrom the plurality of models configured to generate an output predictingat least one characteristic associated with the target product; andproviding a second portion of the sensor data to a second model from theplurality of models configured to generate an output predicting at leastone characteristic associated with the deployment.
 16. The method ofclaim 11, wherein the body of water is an ocean, the providing at leastthe portion of the sensor data as input to at least one model from theplurality of models includes: providing a first portion of the sensordata to a first model from the plurality of models configured togenerate an output predicting at least one characteristic associatedwith the target product; providing a second portion of the sensor datato a second model from the plurality of models configured to generate anoutput predicting at least one characteristic associated with thedeployment; and providing a third portion of the sensor data to a thirdmodel from the plurality of models configured to generate an outputpredicting at least one environmental characteristic associated with aportion of the ocean in which the deployment is deployed.
 17. The methodof claim 11, wherein the body of water is an ocean, the method furthercomprising: predicting, based at least in part on the output of the lastmodel from the plurality of models, a perturbation of at least onecharacteristic associated with a portion of the ocean in which thedeployment is deployed.
 18. The method of claim 11, wherein thedeployment is a first deployment for cultivating a first target product,the method further comprising: selecting, based on the predictedcapacity of the first target product to sequester carbon dioxide, atleast one characteristic associated with a second deployment forcultivating a second target product.
 19. The method of claim 11, whereinthe deployment includes a plurality of cultivation apparatus, theobtaining sensor data includes obtaining the sensor data from at leastone sensor of a cultivation apparatus from the plurality of cultivationapparatus.
 20. A method, comprising: obtaining first sensor data from atleast one sensor associated with a cultivation apparatus included in adeployment of a plurality of cultivation apparatus deployed in an ocean,the deployment configured to cultivate a target product for sequesteringcarbon dioxide; obtaining second sensor data from at least one sensorassociated with the deployment; training a first model based at least inpart on the first sensor data, the first model configured to predict atleast one parameter associated with a growth of the target product ofthe cultivation apparatus; training a second model based at least inpart on the second sensor data, the second model configured to predict ageographic dispersion of the deployment in the ocean; and executing athird model based at least in part on an output from each of the firstmodel and the second model, the third model configured to predict anamount of accumulation of the target product of the deployment.
 21. Themethod of claim 20, wherein the cultivation apparatus is a firstcultivation apparatus, the training the first model includes iterativelytraining the first model based at least in part on a weighted comparisonof the first sensor data and data associated with direct measurements ofthe target product of a second cultivation apparatus included in thedeployment.
 22. The method of claim 21, wherein the training the secondmodel includes iteratively training the second model based at least inpart on a weighted comparison of the second sensor data and dataassociated with at least one of the deployment or the ocean.
 23. Themethod of claim 22, wherein the iteratively training the second modelincludes performing data smoothing of the second sensor data to reduce adimensionality of the second sensor data.
 24. The method of claim 20,wherein the deployment is a first deployment, the method furthercomprising: obtaining third sensor data from at least one sensorassociated with a first cultivation apparatus included in a seconddeployment of a plurality of cultivation apparatus deployed in theocean, the second deployment configured to cultivate a target productfor sequestering carbon dioxide; executing the first model based atleast in part on the third sensor data, an output of the first modelassociated with at least one characteristic of the target product forthe first cultivation apparatus; and executing a fourth model based onan aggregation of the output of the first model and data associated withdirect measurements of the target product of a second cultivationapparatus included in the second deployment, an output of the fourthmodel predicting the at least one parameter associated with the growthof the target product of the plurality of cultivation apparatus includedin the second deployment, an accuracy of the prediction resulting fromthe output of the fourth model being greater than an accuracy of aprediction based on the output of the first model.
 25. The method ofclaim 24, further comprising: obtaining fourth sensor data from at leastone sensor associated with the second deployment; executing the secondmodel based at least in part on the fourth sensor data, and output ofthe second model associated with a geographic dispersion of the seconddeployment in the ocean; and executing a fifth model based on anaggregation of the output of the second model and data associated withat least one of the second deployment or the ocean, an output of thefifth model predicting the geographic dispersion of the seconddeployment in the ocean, an accuracy of the prediction resulting fromthe fifth output being greater than an accuracy of a prediction based onthe second output.
 26. The method of claim 25, further comprising:executing a sixth model based on an aggregation of satellite dataassociated with the ocean and environmental data associated with atleast one cultivation apparatus in the second deployment, an output ofthe sixth model predicting at least one environmental characteristicassociated with a portion of the ocean in which the second deployment isdeployed.
 27. The method of claim 26, further comprising: executing aseventh model based on an aggregation of the outputs of each of thefifth model and the sixth model, an output of the seventh modelpredicting an amount of accumulation of the target product of the seconddeployment.
 28. The method of claim 27, further comprising: executing aneighth model based on an aggregation of the outputs of the fourth modeland the seventh model, an output of the eighth model predicting acapacity of the target product of the second deployment to sequestercarbon dioxide.
 29. The method of claim 28, further comprising:executing a ninth model based at least in part on an output of theeighth model, an output of the ninth model predicting an amount ofcertainty associated with an amount of carbon dioxide sequestered by thetarget product of the second deployment.
 30. The method of claim 29,further comprising: determining an amount of carbon dioxide offsetcredits associated with the amount of carbon dioxide sequestered by thetarget product of the second deployment.